Authbridge Annual Report by Vishal Goyal

BACKGROUND SCREENING IN INDIA TRENDS & INSIGHTS 2016

2 | Trends & Insights 2016

Whatâ&#x20AC;&#x2122;s Inside? Foreword

About the Report

About the Data

Section 1:

12-22

OVERALL DISCREPANCIES

Overall Case-Wise Discrepancy Trend from FY14 to FY16

Overall Check-Wise Discrepancy Trend from FY14 to FY16

Check-Wise Reason for Discrepancy from FY14 to FY16

Address Verification

Education Verification

Employment Verification

Reference Check

Discrepancies in other major checks

Discrepancy Rates with Increase in Number of Checks

Check Discrepancy Trend with Increase in Number of Checks

Education Check Employment Check 24-25

PIECES OF THOUGHTS: Screening in the Age of Global Nomads

Section 2:

26-33

INDUSTRY-WISE ANALYSIS

Industry-Wise Discrepancy in FY16

Industry-Wise Case Discrepancy in FY16

Industry-Wise Case Discrepancy Trend from FY14 to FY16

Industry-Wise Check Discrepancy Trend from FY14 to FY16

Section 3:

34-40

CANDIDATE-WISE ANALYSIS

Gender-Wise Discrepancy Trend

Gender-Wise Case Discrepancy Trend from FY14 to FY16

Gender-Wise Check Discrepancy in FY16

Gender-Wise Check Discrepancy Trend from FY14 to FY16

Age Group-Wise Discrepancy Trend from FY14 to FY16

Check-Wise and Age Group-Wise Discrepancy Trend from FY14 to FY16

Check-Wise and Age Group-Wise Discrepancy Rates in FY16

Trends & Insights 2016 | 3

Section 4:

42-53

VERIFICATION SOURCE-WISE ANALYSIS

Zone-Wise Analysis-Overall

Zone-Wise Analysis: Employment Verification in FY16

Zone-Wise Analysis: Address Verification in FY16

Zone-Wise Analysis: Education Verification in FY16

State-Wise Analysis: Overall

State-Wise Analysis: Employment Verification in FY16

State-Wise Analysis: Address Verification in FY16

State-Wise Analysis: Education Verification in FY16

54-55

PIECES OF THOUGHT: Background Screening Trends to Watch out for in 2016

Section

56-88

DETAILED ANALYSIS OF INDUSTRIES

57-60

IT SOFTWARE / SOFTWARE SERVICES

Overall Case-Wise Discrepancy Trend from FY14 to FY16

Overall Check-Wise Discrepancy Trend from FY14 to FY16

Check-Wise Reason for Discrepancy in FY16

Gender-Wise Discrepancy Trend from FY14 to FY16

Age Group-Wise Discrepancy Trend from FY14 to FY16

61-64

BPO / ITES

Overall Case-Wise Discrepancy Trend from FY14 to FY16

Overall Check-Wise Discrepancy Trend from FY14 to FY16

Check-Wise Reason for Discrepancy in FY16

Gender-Wise Discrepancy Trend from FY14 to FY16

Age Group-Wise Discrepancy Trend from FY14 to FY16

65-68

INTERNET / E-COMMERCE / DOTCOM

Overall Case-Wise Discrepancy Trend from FY14 to FY16

Overall Check-Wise Discrepancy Trend from FY14 to FY16

Check-Wise Reason for Discrepancy in FY16

Gender-Wise Discrepancy Trend from FY14 to FY16

Age Group-Wise Discrepancy Trend from FY14 to FY16

69-72

FINANCIAL SERVICES

Overall Case-Wise Discrepancy Trend from FY14 to FY16

Overall Check-Wise Discrepancy Trend from FY14 to FY16

Check-Wise Reason for Discrepancy in FY16

Gender-Wise Discrepancy Trend from FY14 to FY16

Age Group-Wise Discrepancy Trend from FY14 to FY16

4 | Trends & Insights 2016

73-76

RECRUITMENT / EXECUTIVE SEARCH /

MANPOWER SERVICES / RPO

Overall Case-Wise Discrepancy Trend from FY14 to FY16

Overall Check-Wise Discrepancy Trend from FY14 to FY16

Check-Wise Reason for Discrepancy in FY16

Gender-Wise Discrepancy Trend from FY14 to FY16

Age Group-Wise Discrepancy Trend from FY14 to FY16

77-80

RETAIL

Overall Case-Wise Discrepancy Trend from FY14 to FY16

Overall Check-Wise Discrepancy Trend from FY14 to FY16

Check-Wise Reason for Discrepancy in FY16

Gender-Wise Discrepancy Trend from FY14 to FY16

Age Group-Wise Discrepancy Trend from FY14 to FY16

81-84

TELECOM / ISP / TELECOM INFRASTRUCTURE

Overall Case-Wise Discrepancy Trend from FY14 to FY16

Overall Check-Wise Discrepancy Trend from FY14 to FY16

Check-Wise Reason for Discrepancy in FY16

Gender-Wise Discrepancy Trend from FY14 to FY16

Age Group-Wise Discrepancy Trend from FY14 to FY16

85-88

PHARMA / BIOTECH / CLINICAL RESEARCH

Overall Case-Wise Discrepancy Trend from FY14 to FY16

Overall Check-Wise Discrepancy Trend from FY14 to FY16

Check-Wise Reason for Discrepancy in FY16

Gender-Wise Discrepancy Trend from FY14 to FY16

Age Group-Wise Discrepancy Trend from FY14 to FY16

90-91 CONCLUSION 92-93 ABOUT AUTHBRIDGE

Trends & Insights 2016 | 5

6 | Trends & Insights 2016

TO OUR CLIENTS & FRIENDS

echnology continues to disrupt industries and the way business is conducted. In the late 1980s, as Russia shed its past and prepared to move towards capitalism, Mikhail Gorbachev declared ‘the future is not what it used to be’. Those words ring true for the background screening industry as well. With technology becoming an integral part of how background screening is conducted and intelligent data analytics influencing quality of reports, background screening services is all set to change the way relationships and alliances are created. As the leading global background screening provider we have transformed in the past few years along with the changing industry. The surge in start-up operations with Digital India revolution and the success of disruptive businesses has created an opportunity for background screening service providers to innovate, and scale up to keep pace with the new businesses and think beyond screening employees. I am delighted to present to you the 3rd edition of ‘Background Screening in India – Trends and Insights 2016’. This report contains actionable insights related to transformational recruitment and partnering processes across organizations from different sectors. I am sure that organizations can utilize these insights to create policies and standards to manage verifications at ease. The data shared here is from the volume of business which AuthBridge performs and is representative of the industries and sectors we serve. I hope our benchmarking report will give you insights into employer screening programs, practices and challenges and will help you establish and monitor your background screening processes with greater care and vigilance. Best Regards, Ajay Trehan Founder and CEO, AuthBridge Research Services Pvt. Ltd.

Trends & Insights 2016 | 7

ABOUT THE

REPORT

his report presents trends in discrepancy rates across different background checks and different industries for all cases analyzed by AuthBridge from FY14 to FY16 with special emphasis on cases undertaken in FY16. The first section of the report presents overall discrepancy trends over the last 3 financial years and then gets deeper into reasons behind these discrepancies. A case is tagged ‘discrepant’ if the information supplied is found to be false. The entire report records these discrepancies as a % of the overall cases/checks performed (as stated in each section). It has been observed that overall discrepancy levels have declined gradually in recent years – from 14.13% in FY14 to 10.29% in FY16, indicating that professional background screening helps improve quality of hiring and consistent improvements in background screening practices are needed to match and exceed client expectations. The report covers the most frequently requested checks by clients, namely, Employment, Address, Education and Reference check. Detailed analysis has been presented for discrepancy across each check in this report. Discrepancies continue to be highest in employment check. The next section of the report presents a candidate-wise analysis. Discrepancy rate for male candidates has consistently been higher when compared with discrepancy rate for female candidates. This is followed by a section on zone-wise analysis for India. The last section covers an industry-wise analysis with in-depth take on Top 8 industries on basis of number of cases analyzed. Here, the discrepancy is also dependent on the selection of checks across cases. The report presents analysis based on major checks that were selected by the companies in each industry. Finally, the report concludes with a summary of all the findings and the key takeaways from this year’s analysis.

8 | Trends & Insights 2016

ABOUT THE

DATA

he data for this report has been collated from the cases and checks conducted by AuthBridge in the last three financial years i.e. FY14, FY15 and FY16. The financial year starts from April 1. Detailed analysis is done for the top 8 industries in terms of number of cases received. The Top 8 industries are as given below: 1. IT Software / Software Services 2. BPO / ITES 3. Internet / Ecommerce / Dotcom 4. Financial Services 5. Recruitment / Executive Search / Manpower services / RPO 6. Retail 7. Telecom / ISP / Telecom Infrastructure 8. Pharma / Biotech / Clinical Research. Analysis has been shared for the most conducted checks. The most commonly requested checks are: • Address Verification • Education Verification • Employment Verification • Reference Check A case comprises of various combination of checks commissioned by the clients. If any one of the check is found to be discrepant (any deviation from what the candidate has stated and what is found) then the check will be treated as discrepant. The trend report shows the check-wise and case-wise trend from FY14 to FY16. With every organization following their own definition of what comes under ‘discrepancy’, for the purpose of this study, all case and check conclusions have been mapped to the AuthBridge definition. AuthBridge maps all mismatches to stated antecedents. For this study, data that fall under the category of either ‘insufficiency’ or UTV (unable to verify)has been disregarded. (Insufficiencies are those conclusions where the verification could not be concluded due to either a document or a data requirement which remained unfulfilled till the time the case was concluded. UTV are those instances where despite all efforts to close the verification, either due to the inability of the verification source or due to socio political situations, verification remains inconclusive). Discrepancies reported are not inclusive of such cases and are composite of minor and major discrepant cases analyzed across different industries and different checks.

Trends & Insights 2016 | 9

10 | Trends & Insights 2016

IN EVERY 10 CASES HAS AN ELEMENT OF MISMATCH BETWEEN INFORMATION SUPPLIED & INFORMATION VERIFIED

Trends & Insights 2016 | 11

SECTION 1:

OVERALL

DISCREPANCIES EMPLOYMENT INFORMATION IS THE MOST MANIPULATED ACROSS INDUSTRIES

Among all checks conducted, the discrepancy is found to be highest for Employment Verification. This is followed by Address Verification, Reference Check and Education Verification respectively.

12 | Trends & Insights 2016

1.1

Overall Case-Wise Discrepancy Trend from FY14 to FY16

Figure 1.1 shows the percentage of discrepant cases across the universe of all cases for which background screening was conducted in the last three financial years. For FY16, we observed the discrepancy to be 10.29%. This means that 1 out of every 10 cases misrepresented information.

DISCREPANCIES ARE DECLINING WITH INCREASED AWARENESS AND ADOPTION OF BACKGROUND SCREENING PROCESSES The discrepancy data for cases in Figure 1.1 has shown a decline on an yearly basis from 14.13% in FY14 to 10.29% in FY16. This is a positive sign and signifies the growing formalization of background screening services across industries. However, as a proportion of overall cases, % discrepancy is still very high and calls for the need to conduct rigorous background screening to cover unwanted risks.

FY2016

FY2015

FY2014

Figure 1.1: Case-Wise Discrepancy Trend

14.13%

11.93%

10.29%

Trends & Insights 2016 | 13

1.2

Overall Check-Wise Discrepancy Trend from FY14 to FY16

Figure 1.2 shows the discrepancy trend for the top four checks deployed by clients from different sectors. The discrepancy trend has been represented for cases in the decreasing order of discrepancy from FY14 to FY16.

EMPLOYMENT INFORMATION IS THE MOST MANIPULATED ACROSS INDUSTRIES Among all checks conducted, the discrepancy is found to be highest for Employment Verification. This is followed by Address Verification, Reference Check and Education Verification respectively.

Figure 1.2: Check-Wise Discrepancy Trend

10.06%

6.56%

6.80%

FY2014

4.78%

FY2015

1.38%

4.07%

FY2016

FY2014

1.39%

FY2015

EDUCATION VERIFICATION

FY2016

REFERENCE CHECK

FY2015

FY2016

13.93%

9.25%

FY2016

FY2014 FY2015

14.66%

FY2014

ADDRESS VERIFICATION

EMPLOYMENT VERIFICATION

5.98%

14 | Trends & Insights 2016

1.24%

1.3 Check-Wise Reason for Discrepancy from FY14 to FY16 1.3.1 Address Verification Address verification includes verification of permanent or temporary address for actual residence and tenure of residence. Figure 1.3.1 shows the major reasons contributing to discrepancies in address verification from FY14 to FY16. For the cases analyzed in FY16, the top three reasons contributing to address discrepancy in decreasing order are: • Untraceable address (The address could not be located) • Candidate not residing • House Locked

Figure 1.3.1: Address Verification- Reasons for Discrepancy

2.24%

FY2014

2.03%

2.83%

FY2014

REFEREE REFUSED TO VERIFY

3.39%

1.81%

2.01%

1.74%

FY2015

FY2016

FY2015

FY2014

HOUSE LOCKED

FY2015

1.44%

1.73%

FY2016

2.37%

CANDIDATE NOT RESIDING

0.26%

FY2016

FY2015

FY2014

UNTRACEABLE ADDRESS

0.28%

Please note: Referee refused to verify here means that the person at the stated residence (relatives, parents, neighbors etc.) refused to undergo the verification process

Trends & Insights 2016 | 15

1.3.2 Education Verification Education Verification includes checking education degree for the college / university name, genuineness of issuing authority, status of course completion, year of completion and percentage obtained. Figure 1.3.2 shows the major reasons contributing to discrepancies in education verification from FY14 to FY16. For the cases analyzed in FY16, the top three reasons contributing to education discrepancy in decreasing order are: • Fake / Forged Documents Submitted (The qualification document was fake) • Fake / Unrecognized University (The issuing authority was not recognized) • Incomplete Education (The candidate did not complete the course)

Figure 1.3.2: Education Verification- Reasons for Discrepancy

FY2014

0.92%

FY2014

0.46%

FY2015

0.93%

FY2015

0.41%

0.81%

FY2016

FAKE / UNRECOGNIZED UNIVERSITY

FY2016

FAKE / FORGED DOCUMENTS SUBMITTED

0.33%

FY2014

0.04%

FY2016

0.00%

FY2015

INCOMPLETE EDUCATION

0.10%

16 | Trends & Insights 2016

1.3.3 Employment Verification Employment verification includes verification of employment details for the organization name, designation, role, tenure, reference and more. Figure 1.3.3 shows the major reasons contributing to discrepancies in employment verification from FY14 to FY16. For the cases analyzed in FY16, the top three reasons contributing to employment discrepancy in decreasing order are: • Incorrect Tenure ( The service period for one or more ex-employers was misquoted) • Referee did not respond (This can be due to irrelevant references who are not willing to support the candidate providing the reference or due to lethargy) • Incorrect Remuneration

Figure 1.3.3: Employment Verification -Reasons for Discrepancy

FY2016

4.53%

2.71%

2.12%

2.25%

1.85%

FY2014

1.03%

FY2014

1.07%

FY2015

1.27%

FY2015

1.01%

1.52%

FY2016

NEGATIVE FEEDBACK / PERFORMANCE ISSUES

FY2016

INCORRECT REMUNERATION

FY2014

FY2015

3.84%

FY2015

REFEREE DID NOT RESPOND

FY2016

FY2014

INCORRECT TENURE

1.10%

FY2014

0.59%

FY2014

2.12%

FY2015

0.58%

FY2015

1.87%

0.89%

FY2016

CANDIDATE ABSCONDING

FY2016

FAKE / FORGED DOCUMENTS

0.47%

Trends & Insights 2016 | 17

1.3.4 Reference Check Reference Check includes verification of candidate’s details with reference and checking for performance and general reputation. Figure 1.3.4 shows the major reasons for discrepancies in reference check from FY14 to FY16. For the cases analyzed in FY16, the top three reasons contributing to reference check discrepancy in decreasing order are: • Referee did not respond • Negative feedback / performance issues • Referee refused to verify

Figure 1.3.4: Education Verification- Reasons for Discrepancy

2.68%

0.44%

0.81%

FY2014

0.54%

FY2014

0.28%

FY2015

0.52%

FY2015

0.13%

0.52%

FY2016

INCORRECT / UNCONFIRMED REFEREE

FY2016

REFEREE REFUSED TO VERIFY

FY2014

FY2016

2.99%

0.52%

FY2015

2.20%

NEGATIVE FEEDBACK / PERFORMANCE ISSUES

FY2016

FY2014

REFEREE DID NOT RESPOND

FY2014

CONTRADICTORY INFORMATION PROVIDED

FY2016

FY2015

2.43%

0.71%

0.01%

18 | Trends & Insights 2016

0.05%

1.4 Discrepancies in other major checks Apart from the checks mentioned in previous sections, several other checks are conducted by clients across different sectors. Figure 1.4 shows the discrepancy for other checks as number of discrepancies per million checks. DRUG ABUSE TEST This check scans the candidateâ&#x20AC;&#x2122;s drug profile for any consumption of unacceptable drugs. In FY16, 7319 candidates in a million were found discrepant in drug test cases. IDENTITY VERIFICATION This check identifies if the candidate being screened is the same person as who he / she claims to be. In India, identity check is carried out using Aadhaar, PAN Card, Voter ID, Driving License and Passport. In FY16, 4374 candidates in a million provided fake identities. POLICE VERIFICATION This verification includes screening the candidateâ&#x20AC;&#x2122;s background for any registered police cases in their names. This is conducted by many clients as a compliance requirement and for others to mitigate risks. In FY16, 1814 candidates in a million were found discrepant during police verification process. With digitization of police records, this check is expected to become more effective and vigilant DATABASE CHECK Database verification includes screening candidate profile for any concerns / negative news available in public databases / online records. In FY16, 1034 candidates in a million were found discrepant during database screening. With digitization of records and development of technology, this check is expected to become more popular and standardized.

Figure 1.4: Discrepancies across other checks (in PPM) DISCREPANCY PER MILLION CHECKS

Drug Test

7319

Identity Check

4374

Police Verification

Database Check

1814

1034

Trends & Insights 2016 | 19

1.5 Discrepancy Rates with Increase in Number of Checks Figure 1.5 shows the discrepancy rates with increase in number of checks across cases (A case can involve one or more checks). In FY16, we observe that the accuracy and effectiveness of verification increases and the probability of uncovering discrepancies increases with increase in the number of checks. The graph shows that the discrepancy increases till 4 checks and then reaches maturity. We suggest clients to conduct 4 optimal checks per case.

Figure 1.5: Discrepancies as a function of number of checks conducted NO. OF CHECKS

1 check

4.12%

2 checks

7.56%

3 checks

13.03%

4 checks

14.91%

5 checks

14.93%

More the number of checks conducted, more is the probability of uncovering discrepancies

20 | Trends & Insights 2016

1.6 Check Discrepancy Trend with Increase in Number of Checks 1.6.1 Education Check Figure 1.6.1 depicts the discrepancy trend for education check over last three financial years as a function of number of checks performed on the candidateâ&#x20AC;&#x2122;s education. Data for FY15 and FY16 reveals that with increase in number of checks for education verification, the discrepancy % increased. This indicates that with increase in number of checks, probability of uncovering a discrepancy increases.

Figure 1.6.1: Education Discrepancy as a function of number of checks

FY2014

1.23%

FY2014

FY2015

1.29%

FY2015

1.29%

FY2016

2 CHECKS

FY2016

1 CHECK

3.35%

2.63%

1.54%

2.35%

FY2016

FY2015

FY2014

3 CHECKS

3.17%

2.99%

Trends & Insights 2016 | 21

1.6.2 Employment Check Figure 1.6.2 depicts the discrepancy trend for employment check over last three financial years as a function of number of checks performed on the candidateâ&#x20AC;&#x2122;s employment details. Data for last three financial years reveals that with increase in number of checks for employment verification, the discrepancy % increased. This again indicates that with increase in number of checks, probability of uncovering a discrepancy increases. Figure 1.6.2: Employment Discrepancy as a function of number of checks

FY2014

15.13%

FY2015 FY2016

15.78%

11.04%

19.47%

14.62%

19.72%

15.28%

FY2014

26.33%

FY2015

22.22%

FY2015 FY2016 22 | Trends & Insights 2016

19.98%

4 CHECKS

FY2014

3 CHECKS

FY2016

FY2014

2 CHECKS

FY2015

1 CHECK

22.61%

15.42%

Trends & Insights 2016 | 23

PIECES OF THOUGHTS

SCREENING IN THE AGE OF GLOBAL NOMADS BY AJAY TREHAN

nternational career opportunities are growing dramatically. According to PwC’s “Talent Mobility 2020” report, annual international assignments will increase by 50 per cent between today and 2020-having already grown 25 per cent over the last ten years. The business world is changing rapidly and this has transformed the way the global workforce is sourced, organized and managed. Knowledge, trade, technology, capital and goods are more globally connected than ever before. This has led to a modern concept of global talent management which comes with its own challenges. Companies and executives both share the perception that international assignments are career boosters and therefore coveted. They contribute to a global mindset and help establish global leadership competencies. In the Indian context, it is clear to me that corporate executives with international work experience have a definite edge over those who have remained in the home country. So, with mobility on the rise, the challenge for the background screening industry is going to be to offer foolproof global checks. Some

24 | Trends & Insights 2016

sectors and here I can straightaway mention financial services, IT and even some SMEs will greatly benefit from the ability of verification companies to deliver robust global tracking solutions. As the world moves towards enhanced global mobility, organizations will demand global verification checks that are both quick as well as reliable and, of course, adhere to global laws and regulations. Consider a scenario in which Company ABC is impressed with the background and experience of Mr. Sharma and is keen to hire him for a leadership position. Mr. Sharma worked across geographies; US, China, Brazil and Singapore, apart from his work experience in India. So, while conducting a swift screening of Mr. Sharma, which laws should the background verifier follow? • Those of the country in which Mr.

Sharma’s employer is and has been? • Those of the country in which the

prospective employee is currently based? • Or those of the country where you

are doing his verification?

“

Quality background screeners must have thorough knowledge and understanding of local regulations but beyond that they will also need to have an understanding of cultural challenges

Mr. Sharma’s education too is in not just one country and he has spent extended periods of time in different geographies. What rights do you as Mr. Sharma’s prospective employer have? What rights does Mr. Sharma have? And who will guide you through this complicated process? Well, that’s a conundrum but the answer is rather simple: it’s your background screening partner who will provide the answers and be your friend, philosopher and guide. The complexities of accurately verifying background information are many and that is why you need professionals well acquainted with global laws to diligently sift through the whole process and give you foolproof solutions. Quality background screeners must have thorough knowledge and understanding

of local regulations but beyond that they will also need to have an understanding of cultural challenges. Only then can they aspire to provide global tracking solutions to their clients covering, apart from prospective candidates, franchise partners, suppliers, logistics solutions providers and vendors. Delay in providing effective and reliable background check has the potential to impair business outcomes. Equally, or perhaps more damningly, a less than thorough global tracking solution can deal a severe blow to corporate reputation, hence its integrity must remain non-negotiable, in spite of the complexities and costs of such screening processes.

Trends & Insights 2016 | 25

SECTION 2:

INDUSTRY-WISE ANALYSIS

26 | Trends & Insights 2016

2.1

Industry-Wise Discrepancy in FY16

2.1.1 Industry-Wise Case Discrepancy in FY16 Figure 2.1.1: Industry-Wise discrepancies for cases

15.38%

Financial Services

Internet / e-Commerce / Dotcom

14.75%

11.64%

Retail

Telecom / ISP/ Telecom Infrastructure

10.68%

Industry Average

10.29%

9.00%

Pharma / Biotech / Clinical Research

IT Software / Software Services

7.39%

Recruitment / Executive Search / Manpower Services / RPO

7.12%

BPO / ITES

6.84%

Figure 2.1.1 presents the discrepancy data across industries based on the volume of cases analyzed in FY 2016. Data has been presented for the top 8 industries exhibiting maximum discrepancies. We observe that Financial Services (including both banking and insurance companies) witnessed the maximum discrepancy during the last financial year. This is followed by Internet / e-Commerce / Dotcom companies. Retail and Telecom / ISP / Telecom Infrastructure industries also saw discrepancies above the industry average of 10.29% last financial year.

Trends & Insights 2016 | 27

2.1.2 Industry-Wise Case Discrepancy Trend from FY14 to FY16 Figure 2.1.2 presents the discrepancy trend across top 8 industries based on the volume of cases analyzed in FY16. All industries except IT Software / Software services and Internet / e-Commerce / Dotcom industry witnessed a decline in discrepancies. We will now have a detailed look at each industry and major cause of discrepancies across each of them.

RETAIL

TELECOM / ISP / TELECOM INFRASTRUCTURE

28 | Trends & Insights 2016

FY2016 FY2015 FY2014 FY2016 FY2015 FY2014

INTERNET / ECOMMERCE / DOTCOM

FY2016 FY2015 FY2014

FINANCIAL SERVICES

FY2016 FY2015 FY2014

Figure 2.1.2: Industry-Wise discrepancy trend for cases

14.05% 17.52%

15.38%

8.31% 9.38% 14.75%

13.19% 15.98%

11.64%

25.61% 21.97% 10.68%

RECRUITMENT / EXECUTIVE SEARCH / MANPOWER SERVICES / RPO

BPO / ITES

FY2016 FY2015 FY2014 FY2016 FY2015 FY2014 FY2016 FY2015 FY2014

IT SOFTWARE / SOFTWARE SERVICES

FY2016 FY2015 FY2014

PHARMA / BIOTECH / CLINICAL RESEARCH

21.29% 14.14% 9.00%

9.00% 7.12% 7.39%

27.38% 17.18% 7.12%

13.79% 8.75%

6.84%

Trends & Insights 2016 | 29

2.2

Industry-Wise Check Discrepancy Trend from FY14 to FY16

Figure 2.2: Industry-Wise Discrepancy Trends for Major Checks

IT SOFTWARE / SOFTWARE SERVICES ADDRESS VERIFICATION

FY2016 FY2015 FY2014

EDUCATION VERIFICATION

0.51%

0.74%

0.51%

5.71%

3.78%

2.75%

FY2016 FY2015 FY2014

EMPLOYMENT VERIFICATION

6.97%

9.74%

7.92%

EDUCATION VERIFICATION

ADDRESS VERIFICATION

FY2016 FY2015 FY2014

BPO / ITES

1.57%

1.48%

1.60%

FY2016 FY2015 FY2014

EMPLOYMENT VERIFICATION

30 | Trends & Insights 2016

19.23%

12.87%

8.48%

10.37%

7.32%

5.53%

INTERNET / ECOMMERCE / DOTCOM ADDRESS VERIFICATION

FY2016 FY2015 FY2014

EDUCATION VERIFICATION

0.90%

0.79%

1.62%

6.49%

4.45%

12.83%

FY2016 FY2015 FY2014

EMPLOYMENT VERIFICATION

11.58%

12.58%

10.63%

FINANCIAL SERVICES ADDRESS VERIFICATION

FY2016 FY2015 FY2014

EDUCATION VERIFICATION

1.79%

2.01%

1.53%

7.10%

6.90%

5.68%

FY2016 FY2015 FY2014

EMPLOYMENT VERIFICATION

19.48%

20.09%

15.80%

Figure 2.2 shows the discrepancy trend for major checks conducted across different industries. We observe that except for Internet / e-Commerce / Dotcom industry, all other industries receive maximum discrepancy contribution from employment checks. For Internet / e-Commerce / Dotcom industry, the address discrepancy saw a sharp rise this year and became the cause for maximum discrepancies in this industry. Trends & Insights 2016 | 31

RECRUITMENT / EXECUTIVE SEARCH / MANPOWER SERVICES / RPO

FY2014

2.46%

FY2015

18.76%

12.53%

FY2016

2.05%

FY2016

FY2014

ADDRESS VERIFICATION

FY2015

EDUCATION VERIFICATION

4.05%

9.15%

FY2014

EMPLOYMENT VERIFICATION

FY2015

21.17%

FY2016

19.27%

15.62%

RETAIL

2.66%

FY2014

8.88%

1.63%

FY2015

8.37%

FY2016

8.44%

FY2016

FY2014

ADDRESS VERIFICATION

FY2015

EDUCATION VERIFICATION

1.54%

FY2014

EMPLOYMENT VERIFICATION

FY2016

FY2015

17.35%

32 | Trends & Insights 2016

21.18%

11.13%

TELECOM / ISP / TELECOM INFRASTRUCTURE

FY2014

2.36%

FY2015 FY2016

2.91%

FY2016

FY2014

ADDRESS VERIFICATION

FY2015

EDUCATION VERIFICATION

1.88%

7.96%

2.82%

3.75%

FY2014

EMPLOYMENT VERIFICATION

FY2015

15.81

FY2016

15.56%

8.40%

PHARMA / BIOTECH / CLINICAL RESEARCH

FY2014

0.86%

FY2014

FY2015

0.39%

FY2015

0.33%

FY2016

ADDRESS VERIFICATION

FY2016

EDUCATION VERIFICATION

7.54%

8.77%

7.33%

FY2014

EMPLOYMENT VERIFICATION

FY2016

FY2015

12.47%

16.93%

8.73%

Trends & Insights 2016 | 33

SECTION 3:

CANDIDATE-WISE ANALYSIS

34 | Trends & Insights 2016

3.1

Gender-Wise Discrepancy Trend 3.1.1 Gender-Wise Case Discrepancy Trend from FY14 to FY16 Figure 3.1.1: Gender-Wise Discrepancy Trend from FY14 to FY16

FY14

13.77%

FY15

15.61%

11.19%

FY16

14.05%

7.49%

11.32%

Consistently for the last three financial years (FY14 to FY16), the discrepancy trend across genders for all cases analyzed has reflected higher discrepancy rates for the male candidates in comparison with discrepancy rates for female candidates. This suggests that a male candidate is more likely to misrepresent facts related to his background to improve his chances of getting hired.

3.1.2 Gender-Wise Check Discrepancy in FY16 Figure 3.1.2: Gender-Wise Check Discrepancy in FY16

ADDRESS VERIFICATION

4.72%

5.57%

EDUCATION VERIFICATION

0.64%

1.41%

EMPLOYMENT VERIFICATION

8.36%

10.41%

REFERENCE CHECK

3.43%

4.25%

Figure 3.1.2 shows discrepancy data across male and female candidates for all cases analyzed in FY16. Analysis shows that male candidates are still more likely to produce false information for all major check areas- employment, education, address and reference.

Trends & Insights 2016 | 35

3.1.3 Gender-Wise Check Discrepancy Trend from FY14 to FY16 Figure 3.1.3 shows the discrepancy trends for male and female candidates for major checks conducted on candidates composite of all industries from FY14 to FY16. For all major checks, we observe that males are more likely to fake information compared to females. Figure 3.1.3 Gender-Wise Check Discrepancy Trend from FY14 to FY16 ADDRESS VERIFICATION FY14

10.24%

FY15

9.09%

7.45%

FY16

6.16%

4.72%

5.57%

EDUCATION VERIFICATION FY14

0.74%

FY15

1.55%

0.66%

FY16

1.54%

0.64%

1.41%

EMPLOYMENT VERIFICATION FY14

13.62%

FY15

14.87%

12.75%

FY16

14.09%

8.36%

10.41%

REFERENCE CHECK FY14

5.74%

36 | Trends & Insights 2016

FY15

6.03%

4.92%

FY16

4.73%

3.43%

4.25%

3.2

Age Group-Wise Discrepancy Trend from FY14 to FY16 Figure 3.2(a) Age Group-Wise Discrepancy Trend

FY2016 11.68%

FY2015 13.02%

FY2014 15.15%

FY2016

45-49

13.13%

FY2015

FY2014 12.16%

FY2016

40-44

15.88%

16.15%

18.33%

16.25%

13.80%

FY2015

FY2014

35-39

13.54%

FY2016

FY2015

FY2014

FY2016

18.53%

13.01%

14.98%

10.85%

FY2015

FY2014

FY2016

30-34

25-29

7.18%

FY2015 8.29%

11.45%

FY2014

20-24

Figure 3.2(b) Age Group-Wise Hiring Trend

FY2016

FY2014

FY2015

FY2016

FY2014

FY2015

FY2016

7.16%

5.55%

2.40%

2.41%

0.76%

1.07%

0.93%

45-49

FY2015

40-44

7.11%

6.25%

FY2016

FY2016 36.42%

18.05%

FY2015 36.71%

FY2015

FY2014 35.26%

18.40%

FY2016 35.04%

FY2014

FY2015 34.30%

17.11%

FY2014

35-39

FY2014

30-34

25-29

35.06%

20-24

Figure 3.2(a) shows discrepancies distributed across all age groups for cases analyzed from FY14 to FY16. Discrepancies are higher in age groups >35 primarily because of the low volume of cases analyzed. Figure 3.2(b) shows that volumes of cases being high in age groups 20-34, the discrepancies in these age groups are lower compared to age groups more than 35. This means effect of 1 discrepant case shows higher contribution for age group> 35 compared with age group <35

Trends & Insights 2016 | 37

3.3

Check-Wise and Age Group-Wise Discrepancy Trend from FY14 to FY16 Figure 3.3 shows a detailed analysis of discrepancy for age groups according to the checks from FY14 to FY16. We observe that in age group less than 24, the discrepancy rate for address verification checks is the highest. The same age group has the highest discrepancy rate for employment verification and lowest for education verification.

Figure 3.3 Check-Wise and Age Group-Wise Discrepancy Trend from FY14 to FY16 ADDRESS VERIFICATION

FY2016 5.44%

FY2015 6.06%

FY2014

FY2016 5.79%

45-49

6.58%

FY2015 5.33%

FY2014

FY2016 5.56%

40-44

8.20%

FY2015 5.03%

FY2014

FY2016 5.26%

35-39

6.43%

FY2015 5.65%

FY2014

FY2016 6.01%

7.02%

FY2015 6.34%

FY2014 8.51%

FY2016 7.59%

FY2015

30-34

25-29

11.33%

7.81%

FY2014

20-24

EDUCATION VERIFICATION

38 | Trends & Insights 2016

FY2014

FY2015

FY2016

1.52%

0.86%

FY2016 1.80%

45-49

0.62%

FY2015 1.72%

FY2014

FY2016 1.89%

40-44

2.13%

FY2015 1.83%

FY2014

35-39

2.17%

FY2016 1.66%

FY2015 2.02%

FY2014

FY2016 1.00%

2.31%

FY2015 1.19%

FY2016 0.82%

FY2014

FY2015 0.61%

30-34

25-29

1.17%

FY2014 0.81%

20-24

7.62%

FY2016

FY2015

6.55%

40-44

FY2014

15.34%

15.65%

15.07%

10.70%

9.76%

FY2016

FY2015

FY2014

FY2016

FY2015

FY2014 40-44

6.96%

FY2016

6.50%

35-39

FY2015

16.29%

FY2016

FY2015

14.19% 9.89%

FY2014

14.60%

FY2016

FY2015

13.42% 10.05%

FY2014

13.68%

FY2016

35-39

6.55%

FY2014

FY2016

FY2015

30-34

4.16%

5.42%

FY2014

FY2016

9.59%

FY2015

FY2016

30-34

9.04%

7.15%

4.14%

25-29

FY2015

FY2014

FY2016

13.15%

14.17%

12.37%

FY2015

FY2014

25-29

5.01%

6.55%

3.70%

20-24

FY2015

FY2014

16.30%

17.83% 20-24

4.37%

5.42%

FY2016

FY2015

4.45%

3.72%

FY2014

4.31%

EMPLOYMENT VERIFICATION

45-49

REFERENCE CHECK

45-49

Trends & Insights 2016 | 39

Check-Wise and Age Group-Wise Discrepancy Rates in FY16

3.4

Figure 3.4 shows the discrepancy trend for all age groups across major checks conducted in FY16. Data suggests that discrepancies are highest in employment and address check for the age group 20-24, for education check in the age group 35-39, for reference check in the age group 45-49.

Figure 3.4 Check-Wise and Age Group-Wise Discrepancy Rates in FY16

REFRENCE

EMPLOYMENT

ADDRESS

EDUCATION

7.62% 10.70%

6.50% 9.76%

5.44%

0.86%

REFRENCE

EMPLOYMENT

EDUCATION

ADDRESS 5.79%

4.16%

5.56%

4.14%

45-49

1.80%

REFRENCE

EMPLOYMENT

ADDRESS

40-44

1.89%

EDUCATION

REFRENCE

EMPLOYMENT

35-39

9.89%

9.59%

10.05%

5.26%

6.01% 12.37%

ADDRESS

1.66% 3.70%

3.72% 7.59% 40 | Trends & Insights 2016

EDUCATION

REFRENCE

EMPLOYMENT

ADDRESS

1.00%

EDUCATION

REFRENCE

EMPLOYMENT

ADDRESS

30-34

25-29

0.82%

EDUCATION

20-24

Trends & Insights 2016 | 41

SECTION 4:

VERIFICATION SOURCE-WISE ANALYSIS

42 | Trends & Insights 2016

4.1

Zone-Wise Analysis-Overall

Figure 4.1: Zone-Wise Discrepancy for All Cases

Check-Wise Discrepancy CENTRAL EASTERN

Case-Wise Discrepancy

3.70% 3.54%

NORTHERN

5.01%

SOUTHERN 3.17% WESTERN

CENTRAL

12.98%

EASTERN

12.91%

NORTHERN SOUTHERN

4.88%

WESTERN

13.21% 10.31% 13.19%

Figure 4.1 shows discrepancy data across all zones in India distributed according to checks conducted and cases analyzed during FY 2016. We observe that Northern Zone contributes maximum discrepancy in checks conducted as well as overall cases analyzed followed by Western Zone.

Trends & Insights 2016 | 43

4.1.1 Zone-Wise Analysis: Employment Verification in FY16 Figure 4.1.1 shows that employment discrepancy is the highest in the central zone. The employment discrepancies across different zones in FY16 are: Figure 4.1.1: Zone-Wise Analysis: Employment Verification in FY16

13.39%

CENTRAL

13.22%

EASTERN NORTHERN

10.73%

SOUTHERN

8.07%

WESTERN

11.83%

4.1.2 Zone-Wise Analysis: Address Verification in FY16 Figure 4.1.2 shows that address discrepancy is the highest in the northern zone. The address discrepancies across different zones in FY16 are: Figure 4.1.2: Zone-Wise Analysis: Address Verification in FY16

8.23%

CENTRAL EASTERN

6.45%

NORTHERN SOUTHERN WESTERN 44 | Trends & Insights 2016

8.59% 5.11% 6.83%

4.1.3 Zone-Wise Analysis: Education Verification in FY16 Figure 4.1.3 shows that education discrepancy is the highest in the northern zone. The education discrepancies across different zones in FY16 are: Figure 4.1.3: Zone-Wise Analysis: Education Verification in FY16

CENTRAL

0.96% 1.66%

EASTERN NORTHERN SOUTHERN WESTERN

2.33% 0.36% 0.96%

North India witnessed the maximum discrepant cases in FY16 followed by Western India Trends & Insights 2016 | 45

4.2

State-Wise Analysis: Overall Figure 4.2(a) & (b) shows Top 10 states with highest discrepancies in Indian subcontinent for FY16. The analysis has been presented according to checks conducted and cases analyzed respectively. We observe that Haryana contributes maximum discrepancy in checks conducted followed by Gujarat and Uttar Pradesh. For overall cases analyzed, Gujarat contributes maximum discrepancy in checks conducted followed by Haryana and Uttar Pradesh. Figure 4.2(a) Overall Check-Wise Discrepancy

New Delhi 4.84%

Uttar Pradesh 4.87%

Haryana 5.90% Rajasthan 4.00%

West Bengal 4.23% Gujarat 5.78%

TOP 10 (Check-Wise) Maharashtra 4.81%

Andhra Pradesh (Including Telangana) 3.04%

Karnataka 3.58%

Tamil Nadu 3.06%

46 | Trends & Insights 2016

1. Haryana

5.90%

2. Gujarat

5.78%

3. Uttar Pradesh

4.87%

4. New Delhi

4.84%

5. Maharashtra

4.81%

6. West Bengal

4.23%

7. Rajasthan

4.00%

8. Karnatka

3.58%

9. Tamil Nadu

3.06%

10. Andhra Pradesh

3.04%

Figure 4.2(b) Overall Case-Wise Discrepancy

New Delhi 13.85%

Uttar Pradesh 14.31%

Punjab 14.09% Haryana 15.45%

Rajasthan 12.56%

West Bengal 13.22%

Gujarat 15.81% Maharashtra 13.23%

Karnatka 11.76%

TOP 10 (Case-Wise) 1. Gujarat

15.81%

Chhattisgarh 15.70%

2. Haryana

15.45%

Andhra Pradesh (Including Telangana) 11.35%

3. Uttar Pradesh

14.31%

4. Punjab

14.09%

5. New Delhi

13.85%

6. Maharashtra

13.23%

7. West Bengal

13.22%

8. Rajasthan

12.56%

9. Karnatka

11.76%

10. Andhra Pradesh

11.35%

Haryana & Gujarat amongst the states with both maximum discrepant cases and checks in FY16 Trends & Insights 2016 | 47

4.2.1 State-Wise Analysis: Employment Verification in FY16 Figure 4.2.1 shows discrepancy in employment verification across Top 10 states in India (volume) for FY16.

GUJARAT

16.05%

DELHI

12.67%

WEST BENGAL

11.97%

MAHARASHTRA

10.84%

KERALA

10.36%

HARYANA

9.43%

TAMIL NADU

9.33%

UTTAR PRADESH

9.31%

ANDHRA PRADESH (INCLUDING TELANGANA)

KARNATAKA

48 | Trends & Insights 2016

8.50%

7.30%

We observe that Gujarat contributes maximum employment check discrepancy, followed by Delhi and West Bengal. Figure 4.2.1 State-Wise Employment Discrepancy in FY16 New Delhi 12.67%

Uttar Pradesh 9.31% Haryana 9.43%

West Bengal 11.97% Gujarat 16.05%

Maharashtra 10.84% Andhra Pradesh (Including Telangana) 8.50%

Karnataka 7.30%

Tamil Nadu 9.33% Kerala 10.36%

Trends & Insights 2016 | 49

4.2.2 State-Wise Analysis: Address Verification in FY16 Figure 4.2.2 shows discrepancy in address verification split across Top 10 states in India (volume) for FY16.

PUNJAB

11.99%

HARYANA

9.66%

GUJARAT

8.29%

UTTAR PRADESH

8.26%

DELHI

8.10%

WEST BANGAL

7.97%

MADHYA PRADESH

7.49%

UTTARAKHAND

7.45%

RAJASTHAN

MAHARASHTRA

50 | Trends & Insights 2016

6.99%

6.32%

We observe that Punjab contributes maximum address check discrepancy, followed by Haryana and Gujarat. Figure 4.2.2 State-Wise Address Discrepancy in FY16 New Delhi 8.10%

Uttarakhand 7.45%

Punjab 11.99% Haryana 9.66%

Uttar Pradesh 8.26%

Rajasthan 6.99%

Gujarat 8.29%

Maharashtra 6.32%

Madhya Pradesh 7.49% Andhra Pradesh (Including Telangana) 8.50%

Trends & Insights 2016 | 51

4.2.3 State-Wise Analysis: Education Verification in FY16 Figure 4.2.3 shows discrepancy in education verification split across Top 10 states in India (volume) for FY16.

UTTAR PRADESH

3.89%

BIHAR

3.87%

SIKKIM

3.10%

DELHI

2.09%

RAJASTHAN

1.20%

MAHARASHTRA

0.99%

HARYANA

0.89%

MADHYA PRADESH

0.83%

ANDHRA PRADESH (INCLUDING TELANGANA)

PUNJAB

52 | Trends & Insights 2016

0.62%

0.52%

We observe that Uttar Pradesh contributes maximum education check discrepancy, followed by Bihar and Sikkim. Figure 4.2.3 State-Wise Education Discrepancy in FY16

New Delhi 2.09%

Punjab 0.52% Haryana 0.89%

Uttar Pradesh 3.89%

Bihar 3.87%

Sikkim 3.10%

Rajasthan 1.20%

Maharashtra 0.99%

Madhya Pradesh 0.83% Andhra Pradesh (Including Telangana) 0.62%

Trends & Insights 2016 | 53

PIECES OF THOUGHTS

BACKGROUND SCREENING TRENDS TO WATCH

OUT FOR BY AJAY TREHAN

redicting future trends in any industry has always been a tricky task. While statistics are easily available for predicting some types of business, some depend highly on speculation and guesswork. This is where the background screening industry stands out. The picture here, is crystal clear. When it comes to spotting trends for our industry, my eyes are set firmly on empirical evidence from the recent past. Keeping the evidence in mind, I am formulating my predictions for upcoming trends based on my study of customer needs. It is extremely gratifying to know that the background verification industry has come a long way from fighting for existential reasons to where it is today. Organizations today have finally realized the linkage between background screening and corporate reputation. Corporations, both big and small, are realizing the risk to their reputation because of shaky hiring and onboarding processes. The role of background verification in attaining sustained profitability has finally been established. One of the reasons for the growing need

54 | Trends & Insights 2016

of background verification has been the sudden sprouting of startups. The new age customer is now demanding newer and more technology driven screening solutions. Gone are the days when background checks used to be just obvious and perfunctory checks. Here are some trends, which will stand out more: On-demand or 24x7 economy Weâ&#x20AC;&#x2122;re gradually moving into a time where business will demand instant solutions. In an on-demand economy, compromising on the thoroughness of background verification cannot be an option. As business will evolve, it will also go beyond screening just employees to screening business partners, delivery staff and even house guests. Our new best friend- Technology Thatâ&#x20AC;&#x2122;s right, technology has and will continue to remain our best friend. While making operations cost efficient and reducing turnaround time, it will also eliminate risk. We will stand at a far lesser chance of losing and misusing information, thanks to an automated data repository.

â&#x20AC;&#x153;

Predicting future trends in any industry has always been a tricky task. While statistics are easily available for predicting some types of business, some depend highly on speculation and guesswork

Focus on information security

Keeping an eye around the globe

The industry is set to become highly information sensitive. With the growth in business and client demands, there will arise a need to concoct tight security systems to avoid any data leakage. The need of the hour would be to tighten control and ensure foolproof delivery of sensitive and confidential data.

Needless to say, it is a global economy and it is bound to become even more inter-dependent. Clients in the future will demand thorough global background checks. There is a huge potential for industries like Finance, IT and even SMEs to flourish on the results delivered by background verification companies. Companies which are able to provide reliable global results will become favorites in the industry

Legally speaking In a bid to achieve greater speed and delivering results on time, it is imperative that we as an industry remember to abide by rules and laws. While serving clients from different sectors and geographies, companies with robust knowledge of law will have an edge over the others. Background verification companies will have to prepare themselves to work with aware and alert customers.

Big data Repositories will help companies save a huge deal of time when delivering instant results. Repositories of critical data will help make the screening process much simpler, quicker and more efficient.The businesses that ace the race will be the ones which are able to quickly capitalize on emerging opportunities and are open to innovation. So, letâ&#x20AC;&#x2122;s set the ball rolling.

Trends & Insights 2016 | 55

SECTION 5:

DETAILED ANALYSIS OF

INDUSTRIES 56 | Trends & Insights 2016

5.1

IT Software / Software Services

n 2015, job market grew approximately by 10 per cent. The IT Software/Software Services Industry also witnessed overall increase in recruitment activity in the last financial year. This was fueled by high level of optimism towards growth in India and companies getting ready to add to the government’s initiative towards improving joboriented skills in the country.

Key Findings for this Industry The IT industry is one of the most globally connected industries with people from different walks of life. This industry has a great talent absorbing capacity and accepts talent from different industries. The key findings from cases received last year are: • With increase in recruitment activity, discrepancy in this industry increased in FY16 compared with FY15 • Our analysis reveal that employment records are most manipulated in this industry • Males are more likely to misrepresent information • The age group 30-34 has maximum discrepant cases This entire section provides detailed insights on above findings.

5.1.1 Overall Case-Wise Discrepancy Trend from FY14 to FY16 Figure 5.1.1 shows overall discrepancy across cases received from clients in IT Software / Software services from FY14 to FY16. It is one of the few sectors which has shown a slight increase in the discrepancy level compared with last year. Given that the volume of cases remained nearly the same, the discrepancy in this industry has seen a small increase.

FY2014

Figure 5.1.1 Overall Case-Wise Discrepancy Trend from FY14 to FY16

FY2015

7.12%

FY2016

9.00%

7.39%

Trends & Insights 2016 | 57

5.1.2 Overall Check-Wise Discrepancy Trend from FY14 to FY16 Figure 5.1.2 shows overall check-wise discrepancy trend for IT Software / Software services from FY14 to FY16. Among the four most common checks across industries (employment, education, address and reference), the discrepancy rate is the highest among employment verification for IT Software/ Software services. This is followed by address, reference and education. Improvement in the discrepancy rate in the employment check over the years is also largely responsible for an improvement in the overall discrepancy rate. Figure 5.1.2 Overall Check-Wise Discrepancy Trend from FY14 to FY16

0.51%

REFERENCE CHECK

1.20%

1.94%

FY2016

FY2015

6.97%

58 | Trends & Insights 2016

9.74%

7.92%

FY2016

EMPLOYMENT VERIFICATION

FY2014

0.74%

FY2015

2.75%

FY2014

3.78%

0.51%

FY2015

5.71%

FY2016

EDUCATION VERIFICATION

FY2014

FY2016

FY2015

FY2014

ADDRESS VERIFICATION

2.44%

5.1.3 Check-Wise Reason for Discrepancy in FY16 Having seen the discrepancy trend across major checks, we now present the major reasons of discrepancies across these checks for IT Software/Software services industry in FY16

Figure 5.1.3 Check-Wise Reason for Discrepancy in FY16 ADDRESS VERIFICATION

EDUCATION VERIFICATION

0.72%

Untraceable Address

House Locked

0.66%

Fake / Forged Documents Submitted

0.25%

Fake / Unrecognised University

0.13%

Candidate Not Residing

0.48%

Incorrect Unique Identifier

0.07%

Unable to Verify

0.31%

Incomplete Education

0.04%

Candidate Never Resided

0.22%

University Dissolve

0.02%

Candidate Not Responding

0.14%

University Did Not Respond

0.01%

‘Untraceable address’ and ‘house locked’ remain the two largest reasons for discrepancy in address checks in IT Software/Software services industry.

‘Fake / Forged Documents Submitted’ has been the largest reason for discrepancy in education checks in IT Software / Software services industry.

EMPLOYMENT VERIFICATION

REFERENCE CHECK

Referee Did Not Respond

2.23%

Referee did not Respond

1.75%

Incorrect Tenure

1.60%

Insufficient Information provided

0.25%

Incorrect Remuneration

1.13%

Negative Feedback/ Performance Issues

0.20%

0.16%

No Data Available

0.69%

Referee refused to verify

Negative Feedback/ Performance Issues

0.46%

Incorrect/ Unconfirmed Referee

0.03%

Fake / Forged Documents

0.45%

Contradictory Information Provided

0.01%

‘Referee did not respond’ and ‘incorrect tenure’ are the biggest reasons for discrepancy in employment check in IT Software / Software services industry.

‘Referee did not respond’ remains to be the single biggest reason for discrepancy in reference check in IT Software / Software services industry. Trends & Insights 2016 | 59

5.1.4 Gender-Wise Discrepancy Trend from FY14 to FY16 Figure 5.1.4: Gender-Wise Discrepancy Trend from FY14 to FY16 FY14

6.70%

FY15

10.13%

5.81%

FY16

9.72%

5.39%

9.17%

Figure 5.1.4 shows overall discrepancy across analyzed male and female candidates in the IT Software / Services Industry from FY14 to FY16. In line with the overall trend, male candidates are still more likely to produce false information compared to female candidates. Moreover, the gap between the two discrepancy rates in this sector is significantly higher than the average gap between male and female candidates across other sectors (which is more than 3%).

5.1.5 Age Group-Wise Discrepancy Trend from FY14 to FY16 Figure 5.1.5: Age Group-Wise Discrepancy Trend from FY14 to FY16

FY2014 FY2015 FY2016

45-49

8.86% 9.59% 10.29%

FY2014 FY2015 FY2016 9.47% 9.24% 9.70%

40-44

FY2014 FY2015 FY2016

FY2014 FY2015 FY2016 9.57% 10.43%

FY2014 FY2015 FY2016

35-39

9.79% 8.75%

30-34

25-29

11.68%

11.33%

10.18%

8.06% 8.46%

6.30%

3.54% 3.07%

FY2014 FY2015 FY2016

20-24

Figure 5.1.5 shows overall discrepancy conducted across all analyzed age groups across IT Software and services from FY14 to FY16. Almost all age groups have seen an increase in discrepancies compared with last year with the highest discrepancy in age group 30-34. Again, the fact that lower age groups have lower discrepancies compared with higher age groups is mostly attributed to the fact that the volume of cases analysed are much higher in lower age groups than higher age groups. Hence, contribution of a single discrepancy has more impact on higher age groups than lower age groups.

60 | Trends & Insights 2016

5.2

BPO / ITES

he BPO / ITES Industry underwent a complete disruption with emergence of modern technologies like Cloud, Mobility, Big Data and Machine Learning. The industry is maturing in the country gradually. However, in FY16, this industry witnessed a slight increase in recruitment activity fueled by government’s digital initiatives leading to positive foreign investment sentiments. As a result, the industry saw more business flowing in and hence, the recruitment activities saw a rise.

Key Findings for this Industry The BPO/ ITES industry is truly global with round the clock connectivity and virtually no geographical boundaries. This sector employs young minds in bulk. The key findings from cases analyzed in this sector in FY16 are: • Discrepancy in this industry decreased in FY16 compared with FY15 • Our analysis reveal that employment records are most manipulated in this industry • Males are more likely to misrepresent information • The age group 30-34 has maximum discrepant cases This entire section provides detailed insights on above findings.

5.2.1 Overall Case-Wise Discrepancy Trend from FY14 to FY16 Figure 5.2.1 shows overall discrepancy across cases received from clients in BPO / ITES from FY14 to FY16. This sector has shown a consistent decline in discrepancy rates over the last three years. This can be partly attributed to the fact that this sector was amongst the first to adopt background verification as part of the recruitment process. A part of the reason for the fall in discrepancy is also due to increase in the volume of cases compared to last year. However, the fall in discrepancy is much higher compared to the rise in volume.

FY2014

Figure 5.2.1: Overall Case-Wise Discrepancy Trend From FY14 to FY16

FY2016

FY2015

13.79%

8.75%

6.84%

Trends & Insights 2016 | 61

5.2.2 Overall Check-Wise Discrepancy Trend from FY14 to FY16 Figure 5.2.2 shows overall discrepancy across major checks conducted in BPO / ITES Industry from FY14 to FY16. Among the four most common checks across industries (employment, education, address and reference), the discrepancy rate is the highest among employment verification in this industry. This is followed by reference, address and education check. Figure 5.2.2: Overall Check-Wise Discrepancy Trend from FY14 to FY16

1.60%

12.87%

8.48%

FY2014

13.04%

FY2015

19.23%

FY2015 FY2016 62 | Trends & Insights 2016

1.48%

REFERENCE CHECK

FY2014

EMPLOYMENT VERIFICATION

FY2014

5.53%

FY2015

7.32%

1.57%

FY2016

FY2015

10.37%

FY2016

EDUCATION VERIFICATION

FY2014

ADDRESS VERIFICATION

6.72%

3.56%

5.2.3 Check-Wise Reason for Discrepancy in FY16 Having seen the discrepancy trend across major checks, we now present the major reasons of discrepancies across these checks for BPO/ ITES industry in FY16

Figure 5.2.3: Check-Wise Reason for Discrepancy in FY16 ADDRESS VERIFICATION

EDUCATION VERIFICATION

Candidate Not Residing

1.59%

Candidate Never Resided

1.31%

Untraceable Address

0.84%

Candidate Not Responding House Locked Incomplete / Incorrect Address

0.59%

0.43%

0.28%

Fake / Forged Documents Submitted

0.64%

Fake / Unrecognised University

0.35%

Incorrect Unique Identifier

0.29%

Incomplete Education

0.26%

Documents not Provided

0.01%

Unrecognised Course

0.01%

‘Candidate not residing’ and ‘candidate never resided’ at the stated address remain the two largest reasons for discrepancy in address checks in BPO / ITES industry.

‘Fake / Forged documents’ is the largest reason for discrepancy in education checks in BPO / ITES industry.

EMPLOYMENT VERIFICATION

REFERENCE CHECK

1.26%

Incorrect Designation

Incorrect Tenure

1.15%

Insufficient Information provided

2.40%

Referee refused to verify

0.44%

Incorrect Remuneration

0.78%

Referee did not Respond

0.41%

Negative Feedback/ Performance Issues

0.78%

Negative Feedback/ Performance Issues

0.28%

Fake / Forged Documents

0.74%

FNF / Exit Formality Pending

0.68%

‘Insufficient information provided by the candidate’ is the major reason for discrepancy in the reference check in BPO / ITES industry.

‘Incorrect designation’ and ‘incorrect tenure’ are the major contributors to discrepancies in employment checks in the BPO/ ITES industry. Trends & Insights 2016 | 63

5.2.4 Gender-Wise Discrepancy Trend in from FY14 to FY16 Figure 5.2.4: Gender-Wise Discrepancy Trend from FY14 to FY16 FY14

13.80%

FY15

16.96%

8.23%

FY16

9.84%

5.58%

7.79%

Figure 5.2.4 shows overall discrepancy conducted across analyzed male and female candidates in the BPO / ITES industry from FY14 to FY16. In line with the overall trend, male candidates are still more likely to produce false information compared with female candidates. Moreover, the gap between the two discrepancy rates in this sector has grown compared with the last year.

5.2.5 Age Group-Wise Discrepancy Trend from FY to FY16

5.77% 8.18%

7.91% 10.86%

10.58% 9.43%

10.67% 9.09%

7.57% 10.41%

16.76%

16.77%

17.61%

14.09%

11.21%

45-49

FY2014 FY2015 FY2016

40-44

FY2014 FY2015 FY2016

35-39

FY2014 FY2015 FY2016

30-34

FY2014 FY2015 FY2016

25-29

7.33% 5.92%

20-24

FY2014 FY2015 FY2016

Figure 5.2.5: Age Group-Wise Discrepancy Trend from FY14 to FY16

Figure 5.2.5 shows overall discrepancy across all analyzed age groups in BPO / ITES industry from FY14 to FY16. Data suggests that discrepancies across all age groups have come down drastically compared with previous years. We also note that the discrepancies in this industry increase with age from 20 years to 34 years post which discrepancies go down. This is mostly due to a lot of youth populating this industry.

64 | Trends & Insights 2016

5.3

Internet / e-Commerce / Dotcom

-Commerce industry in India has grown to be a $20 bn industry and is expected to account for 2.5% of the Indian GDP at $300 billion by 2030. Rapid evolution and adoption of technology, penetration of smartphones and internet connectivity, impetus from the government in form of Digital India Campaign and incentives for start-ups and readily available investments have facilitated the growth of this industry. Undoubtedly, this sector has served as a game changer for the Indian Economy. To match its astounding growth rate, this industry registered a huge rise in recruitment activities in FY 2016.

Key Findings for this Industry The Internet / e-commerce / dotcom industry has quickly attracted talent with interesting and empowering job profiles and attractive salaries. The key findings from cases analyzed in this sector in FY16 are: • With growth in overall recruitment, discrepancy in this industry increased by about 60% in FY16 compared with FY15 • Our analysis reveal that address records are most manipulated in this industry, which saw a sharp rise this year • Males are more likely to misrepresent information contrary to females during last financial year • The discrepancy was found to increase with age group in this industry This entire section provides detailed insights on above findings.

5.3.1 Overall Case-Wise Discrepancy Trend from FY14 to FY16 Figure 5.3.1 shows overall discrepancy across cases received from clients in Internet / e-commerce / Dotcom industry from FY 14 to FY16. Contrary to the trend across other industries, this industry has witnessed a substantial increase of about 60% (Y-o-Y) in discrepancies and stands at 14.75% discrepancy in FY16. Despite the fact that volume of cases saw a sharp increase, rise in discrepant cases have been even higher contributing to overall high discrepancy %.

FY2016

FY2015

FY2014

Figure 5.3.1: Overall Case-Wise Discrepancy Trend from FY14 to FY16

8.31%

9.38%

14.75%

Trends & Insights 2016 | 65

5.3.2 Overall Check-Wise Discrepancy Trend from FY14 to FY16 Figure 5.3.2 shows overall discrepancy in major checks conducted across Internet / e-commerce / Dotcom industry from FY14 to FY16. Among the four most common checks across industries (employment, education, address and reference), the discrepancy rate is the highest across address verification in this industry. This is followed by employment, reference and education check. There has been a drastic increase in address check discrepancy this year after the trend reversed from employment discrepancy being the highest contributor in this industry till last year. Figure 5.3.2: Overall Check-Wise Discrepancy Trend from FY14 to FY16

66 | Trends & Insights 2016

0.79%

1.62%

12.58%

10.63%

FY2015

11.58%

FY2014

REFERENCE CHECK

FY2016

FY2015

FY2014

EMPLOYMENT VERIFICATION

FY2014 12.83%

FY2015

4.45%

0.90%

FY2016

EDUCATION VERIFICATION

6.49%

FY2016

FY2015

FY2014

ADDRESS VERIFICATION

2.13%

1.98%

3.61%

5.3.3 Check-Wise Reason for Discrepancy in FY16 Having seen the discrepancy trend for major checks, we now present the major reasons of discrepancies across these checks for Internet / e-commerce / Dotcom industry in FY16

Figure 5.3.3: Check-Wise Reason for Discrepancy in FY16 ADDRESS VERIFICATION

EDUCATION VERIFICATION

Incomplete / Incorrect Address

5.16%

Untraceable Address

4.25%

Candidate Not Responding

1.12%

Incorrect Unique Identifier

0.45%

Fake / Forged Documents Submitted

0.43%

Fake / Unrecognised University

0.38%

House Locked

0.68%

University Dissolve

0.20%

Candidate Never Resided

0.61%

Incomplete Education

0.16%

Referee refused to verify

0.27%

‘Incomplete / Incorrect Address’ and ‘Untraceable Address’ remain the two largest reasons for discrepancy in address checks in Internet / e-Commerce / Dotcom industry.

EMPLOYMENT VERIFICATION

‘Incorrect Unique Identifier’ (meaning incorrect / mismatch in roll number, seat number, year of passing or other parameters used for matching records) and ‘Fake Documents Submitted’ remains the largest reason for discrepancy in education verification in Internet / e-Commerce / Dotcom industry.

REFERENCE CHECK

Incorrect Tenure

2.02%

Referee did not Respond

Incorrect Remuneration

1.97%

Negative Feedback/ Performance Issues

Still Active in Organisation

1.46%

Referee did not respond

1.27%

Fake / Forged Documents

0.86%

Negative Feedback/ Performance Issues

0.73%

2.89%

0.36%

‘Referee did not respond’ is the major reason for discrepancy in reference check in Internet / e-Commerce / Dotcom industry.

‘Incorrect remuneration’ and ‘Incorrect Tenure’ are the two major reasons for employment discrepancy in Internet / e-Commerce / Dotcom industry. Trends & Insights 2016 | 67

5.3.4 Gender-Wise Discrepancy Trend from FY14 to FY16 Figure 5.3.4: Gender-Wise Discrepancy Trend from FY14 to FY16 FY14

7.73%

FY15

9.61%

13.11%

FY16

11.28%

10.75%

12.36%

Figure 5.3.4 shows overall discrepancy for analyzed male and female candidates in the Internet / e-Commerce / Dotcom industry from FY14 to FY16. The discrepancy trend saw a reversal compared with last year. Last year, discrepancy rate across females was more compared to males. However, due to increase in male discrepancy and fall in female discrepancy, this year, males have taken the lead. This implies that a male candidate is more likely to misrepresent information. Point to note here is that although the overall discrepancy in this industry is 14.75%, the discrepancy data for both male and female candidates reported for FY16 is lower than 14.75%. This disaccord is due to missing gender information across many cases analyzed.

5.3.5 Age Group-Wise Discrepancy Trend from FY to FY16

40-44

FY2014 FY2015 FY2016

45-49

FY2014 FY2015 FY2016

35-39

19.09%

12.50% 13.11%

15.00% 16.72%

18.48%

11.65% 13.35%

16.86% 18.29%

12.03% 15.51%

14.42%

10.31%

9.09%

7.55% 8.25%

6.46%

FY2014 FY2015 FY2016

30-34

FY2014 FY2015 FY2016

25-29

FY2014 FY2015 FY2016

20-24

FY2014 FY2015 FY2016

Figure 5.3.5: Age Group-Wise Discrepancy Trend from FY14 to FY16

Figure 5.3.5 shows overall discrepancy across all analyzed age groups across Internet / e-Commerce / Dotcom Industry from FY14 to FY16. Discrepancy rates across all age groups have seen an increase in line with increase in overall discrepancy across this industry. This was due to rapid growth of the sector. We also note that this discrepancy trend increases with age till 39 years, implying that with increase in experience, candidates tend to fake information more. Moreover, discrepancies are higher in age groups >30 primarily because of the low volume of cases analyzed, making the effect of 1 discrepant case more powerful compared to contribution from age group <30 68 | Trends & Insights 2016

5.4

Financial Services

he Financial Services Industry is one such industry where customer service and trust is of prime importance. Today, the Financial Services industry is at the brink of disruptive innovation. From FinTech and the Internet of Things to cyber risk and the changing demographics of wealth, the dominance of financial services is under threat. The industry has overcome challenges and is giving it a tough fight by adopting technology at a fast pace to deliver seamless customer experience. To build a strong customer experience, the recruitment activity in financial services industry saw a sharp rise in FY16.

Key Findings for this Industry In our analysis, the financial services industry considered is composite of banking and insurance companies. We observe that the Financial Services Industry has the highest discrepancy compared with all other industries. The key findings from cases analyzed in this sector in FY16 are: • Discrepancy in this industry decreased slightly in FY16 compared with FY15 • Our analysis reveal that employment records are most manipulated in this industry • Males are more likely to misrepresent information • Maximum discrepant cases were found in age bracket 30-34 years This entire section provides detailed insights on above findings.

5.4.1 Overall Case-Wise Discrepancy Trend from FY14 to FY16 Figure 5.4.1 shows overall discrepancy across cases received from clients in Financial Services Industry from FY14 to FY16. This is inclusive of banks and insurance companies. The discrepancy rate in this sector has seen a small downfall, majorly due to increase in volumes. Still the discrepancy rate at 15.38% is the highest amongst all industries.

14.05%

FY2016

FY2015

FY2014

Figure 5.4.1: Overall Case-Wise Discrepancy Trend from FY14 to FY16

17.52%

15.38%

Trends & Insights 2016 | 69

5.4.2 Overall Check-Wise Discrepancy Trend from FY14 to FY16 Figure 5.4.2 shows overall discrepancy for major checks conducted across Financial Services Industry from FY14 to FY16. Among the four most common checks across industries (employment, education, address and reference), the discrepancy rate is the highest in employment verification in this industry. This is followed by address, reference and education check. The Employment and reference discrepancies in this sector are pretty high compared with other industries. Figure 5.4.2: Overall Check-Wise Discrepancy Trend from FY14 to FY16

FY2014

EDUCATION VERIFICATION

7.01%

5.88%

2.62%

2.13%

FY2014

0.00%

20.90%

FY2015

0.00%

70 | Trends & Insights 2016

17.37%

FY2016

20.76%

FY2016

FY2014

REFERENCE CHECK

FY2015

EMPLOYMENT VERIFICATION

3.34%

FY2015

9.79%

FY2016

FY2015

FY2014

ADDRESS VERIFICATION

9.76%

5.4.3 Check-Wise Reason for Discrepancy in FY16 Having seen the discrepancy trend for major checks, we now present the major reasons of discrepancies across these checks in Financial Services Industry in FY16 Figure 5.4.3: Check-Wise Reason for Discrepancy in FY16

ADDRESS VERIFICATION

EDUCATION VERIFICATION

Fake / Forged Documents Submitted

2.19%

House Locked

0.69%

Untraceable Address

0.85%

Fake / Unrecognised University

0.43%

UTV

0.85%

Incorrect Unique Identifier

0.27%

Candidate Never Resided

0.48%

Incomplete Education

0.07%

Candidate Not Residing

0.46%

Documents not Provided

0.04%

Incomplete / Incorrect Address

0.40%

Unrecognised Course

0.02%

‘House locked’ remains as the largest reason for discrepancy in address checks in Financial Services Industry.

‘Fake / Forged documents submitted’ and ‘Fake / unrecognized University’ remain the two largest reasons for discrepancy in education checks in Financial Services Industry.

EMPLOYMENT VERIFICATION

REFERENCE CHECK

3.75%

Incorrect Tenure Referee did not Respond

1.72%

Insufficient Information provided

2.03%

Referee did not Respond

1.48%

Insufficient Information provided

1.70%

Negative Feedback/ Performance Issues

1.48%

Negative Feedback/ Performance Issues

1.63%

Referee refused to verify

0.37%

FNF / Exit Formality Pending

1.59%

Fake / Forged Documents

1.30%

‘Insufficient information provided’ remains to be the single biggest reason for discrepancy in reference check. ‘Referee did not Respond’ and ‘Negative Feedback/Performance issues’ are yet other big reasons for discrepancy in reference checks in Financial Services Industry.

‘Incorrect Tenure’ remains to be the single biggest reason for discrepancy in employment check in Financial Services Industry. Trends & Insights 2016 | 71

5.4.4 Gender-Wise Case Discrepancy Trend in from FY14 to FY16 Figure 5.4.4: Gender-Wise Case Discrepancy Trend from FY14 to FY16 FY14

12.01%

FY15

14.50%

14.06%

FY16

18.81%

10.15%

17.28%

Figure 5.4.4 shows overall discrepancy for analysed male and female candidates in Financial Services Industry from FY14 to FY16. In line with the overall trend, male candidates are still more likely to produce false information compared with female candidates. Moreover, the gap between the two discrepancy rates in this sector is significantly higher than the average gap between male and female candidates across other sectors (which is more than around 3%). Point to note here is that although the overall discrepancy in this industry is 19.02%, the discrepancy data for both male and female candidates reported for FY16 is lower than 19.02%. This disaccord is due to missing gender information across many cases analyzed.

5.4.5 Age Group-Wise Case Discrepancy Trend from FY14 to FY16 Figure 5.4.5: Age Group-Wise Case Discrepancy Trend from FY14 to FY16 45-49

FY2014 FY2015 FY2016

40-44

FY2014 FY2015 FY2016

FY2014 FY2015 FY2016 26.74% 25.49%

35-39

FY2014 FY2015 FY2016

30-34

25.09% 26.90% 23.46%

FY2014 FY2015 FY2016

25-29

11.98% 14.01% 10.37%

22.96% 19.18% 24.53%

21.74%

13.83% 18.75% 16.70%

7.01% 8.42% 7.80%

FY2014 FY2015 FY2016

20-24

Figure 5.4.5 shows overall discrepancy conducted for all analysed age groups in Financial Services Industry from FY14 to FY16. Although all age groups have seen a decrease in discrepancies compared to last year, the discrepancies are still higher compared with other industries; the highest discrepancy contributed by people in age group 30-34. Moreover, data suggests that discrepancies increase with age till 34 years, post which discrepancies go down suggesting that people in midway in their careers fake more information in Financial services sector. Again, the fact that lower age groups have lower discrepancies compared with higher age groups is mostly attributed to the fact that the volume of cases analysed are much higher in lower age groups than higher age groups. Hence, contribution of a single discrepancy has more impact on higher age groups than lower age groups. 72 | Trends & Insights 2016

5.5

Recruitment / Executive Search / Manpower services / RPO

ffective organizations today are built around highly empowered employees. The dynamics of talent search is evolving as companies look beyond traditional resume-based recruitment. Companies are now looking at employees as brand ambassadors. With businesses expanding like never before, demand for talent has seen a dramatic increase. This has led to tremendous growth of recruitment/ executive search and manpower services providers. The rapidly changing global economic stage has led to quick adaptations in the executive search industry, including leveraging new opportunities using social media and technology.

Key Findings for this Industry With candidates embracing digital platforms and employee loyalty on a decline, the executive search industry has seen an increase in demand. The key findings from cases analyzed in this sector in FY16 are: • Discrepancy in this industry decreased slightly in FY16 compared with FY15 • Our analysis reveal that employment records are most manipulated in this industry • Males are more likely to misrepresent information • Discrepancies were found to increase with increase in age and experience This entire section provides detailed insights on above findings.

5.5.1 Overall Case-Wise Discrepancy Trend from FY14 to FY16 Figure 5.5.1 shows overall discrepancy across cases received from clients in Recruitment / Executive Search / Manpower services / RPO Industry from FY14 to FY16. This sector has seen a massive decrease in overall discrepancy compared with the previous year, about 60% decrease Y-O-Y. This is partly due to decrease in volume of cases analyzed by us this year. This also implies the formalization of background screening and awareness about the process amongst candidates in this industry

FY2014

Figure 5.5.1: Overall Case-Wise Discrepancy Trend From FY14 to FY16

FY2016

FY2015

27.38%

17.18%

7.12%

Trends & Insights 2016 | 73

5.5.2 Overall Check-Wise Discrepancy Trend from FY14 to FY16 Figure 5.5.2 shows overall discrepancy across major checks conducted across Recruitment / Executive Search / Manpower services / RPO Industry from FY14 to FY16. Among the four most common checks across industries (employment, education, address and reference), the discrepancy rate is the highest in employment verification in this industry. This is followed by reference, address and education. Again, data suggests that reference check discrepancy is considerably higher compared with other industries. This implies that candidates provide inappropriate or misleading references to make their way into this industry. Figure 5.5.2: Overall Check-Wise Discrepancy Trend from FY14 to FY16

9.15%

19.27%

15.62%

FY2014 FY2014 FY2015

21.17%

FY2015 FY2016

4.05%

REFERENCE CHECK

FY2014

EMPLOYMENT VERIFICATION

74 | Trends & Insights 2016

2.46%

FY2016

12.53%

2.05%

FY2016

FY2015

18.76%

FY2015

EDUCATION VERIFICATION

FY2014

ADDRESS VERIFICATION

12.59%

11.01%

12.01%

5.5.3 Check-Wise Reason for Discrepancy in FY16 Figure 5.5.3: Check-Wise Reason for Discrepancy in FY16 Having seen discrepancy trends for major checks, we now present the major reason for discrepancies for recruitment / executive search / man power services / RPO

ADDRESS VERIFICATION

EDUCATION VERIFICATION

2.97%

Untraceable Address Candidate Not Residing

2.35%

Fake / Forged Documents Submitted

1.40%

Fake / Unrecognised University

1.19%

1.05%

Incomplete / Incorrect Address

1.10%

Incorrect Unique Identifier

Candidate Never Resided

0.93%

Incomplete Education

0.14%

Referee refused to verify

0.56%

Fake/Forged Documents

0.07%

Candidate Left Organisation

0.49%

Incomplete/Incorrect Address

0.07%

‘Untraceable address’ and ‘candidate not residing’ have been the largest reasons for discrepancy in address verification in Recruitment / Executive Search / Manpower services / RPO Industry.

‘Fake/forged documents submitted’ and ‘fake / unrecognized university’ have been the biggest reasons for education discrepancy in Recruitment / Executive Search / Manpower services / RPO Industry.

EMPLOYMENT VERIFICATION

REFERENCE CHECK

Incorrect Tenure

3.43%

Referee did not Respond

Incorrect Remuneration

3.05%

Negative Feedback/ Performance Issues

Referee did not Respond

1.42%

Fake / Forged Documents

1.25%

Negative Feedback/ Performance Issues

1.14%

Fake Employment

6.71%

2.39%

Referee refused to verify

1.15%

Insufficient Information provided

0.97%

‘Referee did not respond’ has been the largest reason for discrepancy in reference check in Recruitment / Executive Search / Manpower services / RPO Industry.

0.87%

‘Incorrect tenure’ and ‘Incorrect Remuneration’ have been the major reasons for employment discrepancy in Recruitment / Executive Search / Manpower services / RPO Industry. Trends & Insights 2016 | 75

5.5.4 Gender-Wise Discrepancy Trend from FY14 to FY16 Figure 5.5.4: Gender-Wise Discrepancy Trend from FY14 to FY16 FY14

26.30%

FY15

28.10%

16.19%

FY16

18.36%

5.43%

8.17%

Figure 5.5.4 shows overall discrepancy for analysed male and female candidates across Recruitment / Executive Search / Manpower services / RPO Industry from FY14 to FY16. In line with the overall trend, male candidates are still more likely to produce false information compared with female candidates. The overall discrepancies across both genders, however, have come down drastically compared with previous years.

5.5.5 Age Group-Wise Discrepancy Trend from FY14 to FY16

40-44

FY2014 FY2015 FY2016

12.21%

16.42% 14.67% 15.50%

45-49

FY2014 FY2015 FY2016

35-39

FY2014 FY2015 FY2016

30-34

FY2014 FY2015 FY2016

25-29

11.61% 16.67% 33.33%

27.27%

25.99% 21.89%

28.73%

26.95%

19.51%

17.72%

15.70%

11.97%

8.64%

20-24

FY2014 FY2015 4.48% FY2016

Figure 5.5.5: Age Group-Wise Discrepancy Trend from FY14 to FY16

Figure 5.5.5 shows overall discrepancy for all analysed age groups in Recruitment / Executive Search / Manpower services / RPO Industry from FY14 to FY16. The discrepancy rates have decreased drastically across all ages. Interestingly, in this sector, discrepancies grew with age implying that with growing experience, candidates fake more information. 76 | Trends & Insights 2016

5.6

Retail

he Indian retail industry is amongst the most dynamic industries in the country. It accounts for over 10 per cent of the country’s Gross Domestic Product (GDP) and around 8 per cent of the employment. This industry has seen entry from a lot of new players. This has been further facilitated by positive investor confidence and much needed impetus from the government. Of late, this sector has seen stiff competition from e-commerce players. The recruitment activity in this industry also saw a rise in the last financial year. However, the traditional retail segment has seen a decline.

Key Findings for this Industry In our analysis, the ecommerce companies have been separated from the retail companies. Companies which are purely into retail have been accounted here. The key findings from cases analyzed in this sector in FY16 are: • Discrepancy in this industry decreased in FY16 compared with FY15 • Our analysis reveal that employment records are most manipulated in this industry • Interestingly, in this sector, females are more likely to misrepresent information • Discrepancies were found to increase with increase in age and experience This entire section provides detailed insights on above findings.

5.6.1 Overall Case-Wise Discrepancy Trend from FY14 to FY16 Figure 5.6.1 shows overall discrepancy across cases received from clients in Retail Industry from FY14 to FY16. Despite increase in volume of cases analyzed, the volume of discrepant cases have decreased this year, leading to a lower discrepancy rate compared to last year.

13.19%

FY2016

FY2015

FY2014

Figure 5.6.1: Overall Case- Wise Discrepancy Trend from FY14 to FY16

15.98%

11.64%

Trends & Insights 2016 | 77

5.6.2 Overall Check-Wise Discrepancy Trend from FY14 to FY16 Figure 5.6.2 shows overall discrepancy across major checks conducted in Retail Industry from FY14 to FY16. Among the four most common checks across industries (employment, education, address and reference), the discrepancy rate is the highest in employment verification in this industry. This is followed by address, reference and education check. Figure 5.6.2: Overall Check-Wise Discrepancy Trend from FY14 to FY16

FY2014

2.66%

8.37%

FY2015

1.63%

8.44

FY2016

1.54%

FY2014

17.35%

FY2015 FY2016 78 | Trends & Insights 2016

REFERENCE CHECK

21.18%

11.13%

10.28%

FY2015

FY2014

EMPLOYMENT VERIFICATION

FY2016

FY2014

8.88%

FY2015

EDUCATION VERIFICATION

FY2016

ADDRESS VERIFICATION

16.30%

4.26%

5.6.3 Check-Wise Reason for Discrepancy in FY16 Having seen the discrepancy trend across major checks, we now present the major reasons of discrepancies across these checks for Retail Industry in FY16

Figure 5.6.3: Check-Wise Reason for Discrepancy in FY16 ADDRESS VERIFICATION

EDUCATION VERIFICATION

Candidate Not Responding

4.52%

Candidate Not Residing

0.96%

Incorrect Unique Identifier

0.55%

Fake / Forged Documents Submitted

0.50%

0.40%

House Locked

0.64%

Fake/Unrecognised University

Incomplete / Incorrect Address

0.58%

Incomplete Education

0.05%

Candidate Never Resided

0.55%

University did not Respond

0.05%

‘Candidate not responding’ has been the largest reason for discrepancy in address checks in Retail industry.

‘Incorrect unique identifier (meaning incorrect/mismatch in roll number, seat number, year of passing or other parameters used for matching records)’ and ‘fake / forged documents submitted’ are the prominent reasons for discrepancy in education verification checks in Retail industry.

EMPLOYMENT VERIFICATION

REFERENCE CHECK

Referee refused to verify

0.37%

3.35%

Incorrect Tenure Incorrect Remuneration

2.33%

Negative Feedback/ Performance Issues Still Active in Organisation

1.20%

0.84%

Fake / Forged Documents

0.78%

Notice Period not served

0.66%

Referee did not Respond

1.60%

Insufficient Information provided

0.80%

Negative Feedback/ Performance Issues

0.80%

Referee refused to verify

0.80%

‘Referee did not respond’ during the verification process has emerged as the most prominent reason for discrepancy in reference check in the Retail industry.

‘Incorrect tenure’ has been the single biggest reason for discrepancies in employment checks in the retail industry. Trends & Insights 2016 | 79

5.6.4 Gender-Wise Discrepancy Trend from FY14 to FY16 Figure 5.6.4: Gender-Wise Discrepancy Trend from FY14 to FY16 FY14

14.29%

FY15

12.55%

29.13%

FY16

15.83%

13.83%

12.42%

Figure 5.6.4 shows overall discrepancy for analysed male and female candidates across Retail Industry from FY14 to FY16. Interestingly, for this sector, female discrepancies were found to be higher than male discrepancies. This implies that females in retail industry fake information more compared to male candidates. This is also because the retail sector employs female candidates in volumes. However, this year, the gap between male and female discrepancies in this sector is very small, especially when compared with other industries.

5.6.5 Age Group-Wise Discrepancy Trend from FY14 to FY16

9.52%

FY2014 FY2015 FY2016

13.95%

12.70%

12.62% 17.65%

33.33%

21.12%

18.75% 22.18%

45-49

FY2014 FY2015 FY2016

40-44

12.63%

FY2014 FY2015 FY2016 16.56%

12.25%

FY2014 FY2015 FY2016 13.81%

35-39

11.45%

30-34

25-29

17.71%

12.06% 12.75% 11.36%

FY2014 FY2015 FY2016

20-24

FY2014 FY2015 FY2016

Figure 5.6.5: Age Group-Wise Discrepancy Trend from FY14 to FY16

Figure 5.6.5 shows overall discrepancy for all analysed age groups in Retail Industry from FY14 to FY16. Interesting, in this industry, the discrepancy rates are almost same across all age groups; age groups 25-34 being slightly higher. Again, in the case of age group 45-49, we see a steep rise in discrepancy due to low volume of cases analysed. In such cases, a few discrepant cases can have significant contribution to overall discrepancy compared with other age groups. 80 | Trends & Insights 2016

5.7

Telecom / ISP / Telecom Infrastructure

he Telecom / ISP / Telecom Infrastructure sector continues to be at the epicenter for growth and innovation. Mobile devices and broadband connectivity grew to become more embedded in the fabric of society. With more focus on technology, this sector through its evolved offerings in video streaming, Internet of Things (IoT), and mobile payments has been driving momentum in the Indian business landscape in FY 2016. The recruitment in this industry, however, saw a decline owing to more focus on automation and technology.

Key Findings for this Industry The key findings from cases analyzed in this sector in FY16 are: • Discrepancy in this industry decreased in FY16 compared with FY15 • Our analysis reveal that employment records are most manipulated in this industry • Males are more likely to misrepresent information • Discrepancies were found to increase with increase in age and experience This entire section provides detailed insights on above findings.

5.7.1 Overall Case-Wise Discrepancy Trend from FY14 to FY16 Figure 5.7.1 shows overall discrepancy across cases received from clients in Telecom / ISP / Telecom Infrastructure Industry from FY14 to FY16. Despite the increase in volume of cases received, this sector has also witnessed a fall in overall discrepancy like other industries this year.

FY2014

Figure 5.7.1: Overall Case-Wise Discrepancy Trend From FY14 to FY16

FY2016

FY2015

25.61%

21.97%

10.68%

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5.7.2 Overall Check-Wise Discrepancy Trend from FY14 to FY16 Figure 5.7.2 shows overall discrepancy for major checks conducted across Telecom / ISP / Telecom Infrastructure Industry from FY14 to FY16. Among the four most common checks across industries (employment, education, address and reference), the discrepancy rate is the highest in employment verification in this industry. This is followed by address and education check. Employment discrepancy saw a significant decrease in discrepancy this year. Reference check cases were low in volume and hardly saw any discrepancies this year. Figure 5.7.2: Overall Check-Wise Discrepancy Trend from FY14 to FY16

2.87%

3.75%

2.91%

2.36%

1.88%

FY2014

15.56%

FY2015

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8.40%

FY2016

15.81%

FY2016

FY2014

REFERENCE CHECK

FY2015

EMPLOYMENT VERIFICATION

FY2014

7.96%

FY2015

EDUCATION VERIFICATION

FY2016

FY2015

FY2014

ADDRESS VERIFICATION

4.69%

9.88%

0.00%

5.7.3 Check-Wise Reason for Discrepancy in FY16 Having seen the discrepancy trend across major checks, we now present the major reasons of discrepancies across these checks in Telecom / ISP / Telecom Infrastructure Industry in FY16

Figure 5.7.3: Check-Wise Reason for Discrepancy in FY16 ADDRESS VERIFICATION

EDUCATION VERIFICATION

Candidate Not Residing

1.06%

Untraceable Address

1.00%

Candidate Never Resided

0.79%

Candidate Not Responding

0.11%

Incomplete / Incorrect Address

0.11%

0.84%

Fake / Unrecognised University

0.52%

Incorrect Unique Identifier

0.44%

University Dissolved

0.48%

Candidate Left Organisation

Fake / Forged Documents Submitted

0.08%

‘Fake / Forged documents submitted’ has emerged as the largest reason for discrepancy in education verification in Telecom / ISP / Telecom Infrastructure Industry.

‘Candidate not residing’ and ‘untraceable address’ provided by the candidate have been the most prominent reasons for address discrepancy in Telecom / ISP / Telecom Infrastructure Industry.

EMPLOYMENT VERIFICATION

2.05%

Incorrect Tenure Referee did not Respond

1.25%

Still Active in Organisation

1.23%

Incorrect Remuneration

‘Incorrect tenure’ has been the single biggest reason for discrepancies in employment checks in Telecom / ISP / Telecom Infrastructure industry. Due to low volumes, no discrepancies were found in Reference checks. Hence, no data is available for this case.

0.91%

Negative Feedback/ Performance Issues

0.71%

FNF / Exit Formality Pending

0.58%

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5.7.4 Gender-Wise Discrepancy Trend from FY14 to FY16 Figure 5.7.4: Gender-Wise Discrepancy Trend from FY14 to FY16 FY14

21.11%

FY15

25.88%

19.78%

FY16

22.15%

7.67%

11.87%

Figure 5.7.4 shows overall discrepancy across analysed male and female candidates in Telecom / ISP / Telecom Infrastructure Industry from FY14 to FY16. In line with the overall trend, male candidates are still more likely to produce false information compared to female candidates. Moreover, the gap between the two discrepancy rates in this sector is slightly higher than the average gap between male and female candidates across other sectors (which is more than around 3%).

5.7.5 Age Group-Wise Discrepancy Trend from FY14 to FY16

FY2014 FY2015 FY2016

FY2014 FY2015 FY2016 25.33% 23.53%

21.05% 20.00% 25.00%

12.12% 24.93% 22.57%

22.64% 26.22%

22.08%

18.01% 18.25%

27.56%

45-49

8.09%

40-44

11.41%

10.03%

35-39

FY2014 FY2015 FY2016

30-34

FY2014 FY2015 FY2016

25-29

8.23%

20-24

FY2014 FY2015 FY2016

Figure 5.7.5: Age Group-Wise Discrepancy Trend from FY14 to FY16

Figure 5.7.5 shows overall discrepancy for all analysed age groups in Telecom / ISP / Telecom Infrastructure Industry from FY14 to FY16. While discrepancy rates have decreased across all age groups, it has shown a significant increase in the age group 4549. Moreover, the trend shows that discrepancy rates increase with increase in age and experience till 39 years. Again, the fact that lower age groups have lower discrepancies compared with higher age groups is mostly attributed to the fact that the volume of cases analysed are much higher in lower age groups than higher age groups. Hence, contribution of a single discrepancy has more impact on higher age groups than lower age groups.

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5.8

Pharma / Biotech / Clinical Research

ndia is the largest supplier of generic drugs to the globe. Indian generics account for 20 per cent of global exports in terms of volume. The pharma / biotech / clinical research industry is highly fragmented. Consequently, consolidation has become an important characteristic of the Indian pharmaceutical market. The recruitment activities in the Pharma / Biotech/Clinical research saw a decline in the last financial year owing to consolidation across the industry.

Key Findings for this Industry The key findings from cases analyzed in this sector in FY16 are: • Discrepancy in this industry decreased slightly in FY16 compared with FY15 • Our analysis reveal that employment records are most manipulated in this industry • Males are more likely to misrepresent information • The maximum discrepant cases were found to belong to the age bracket 35-39 years This entire section provides detailed insights on above findings.

5.8.1 Overall Case-Wise Discrepancy Trend from FY14 to FY16 Figure 5.8.1 shows overall discrepancy across cases received from clients in Pharma / Biotech / Clinical Research Industry from FY14 to FY16. Despite the increase in volume of cases received, the sector witnessed a significant decline in discrepancy rate compared to last year.

FY2014

Figure 5.8.1: Overall Case-Wise Discrepancy Trend From FY14 to FY16

FY2016

FY2015

21.29%

14.14%

9.00%

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5.8.2 Overall Check-Wise Discrepancy Trend from FY14 to FY16 Figure 5.8.2 shows overall discrepancy for major checks conducted across Pharma / Biotech / Clinical Research Industry from FY14 to FY16. Among the four most common checks across industries (employment, education, address and reference), the discrepancy rate is the highest in employment verification in this industry. This is followed by address, reference and education check. Figure 5.8.2: Overall Check-Wise Discrepancy Trend from FY14 to FY16

FY2015 FY2016

16.93%

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0.33%

REFERENCE CHECK

12.47%

8.73%

0.39%

FY2014

EMPLOYMENT VERIFICATION

FY2014

7.33%

FY2015

FY2016

8.77%

0.86%

22.37%

FY2015

7.54%

FY2016

EDUCATION VERIFICATION

FY2016

FY2014

ADDRESS VERIFICATION

13.35%

3.86%

5.8.3 Check-Wise Reason for Discrepancy in FY16 Having seen the discrepancy trend across major checks, we now present the major reasons of discrepancies across these checks for Pharma / Biotech / Clinical Research Industry in FY16

Figure 5.8.3: Check-Wise Reason for Discrepancy in FY16 ADDRESS VERIFICATION

EDUCATION VERIFICATION

Candidate Not Residing

1.38%

Fake / Unrecognised University

0.19%

Candidate Not Responding

1.35%

Fake / Forged Documents Submitted

0.09%

House Locked

1.32%

Incorrect Unique Identifier

0.05%

Candidate Never Resided

1.10%

Untraceble Address

0.91%

Candidate Left Organisation

0.47%

‘Fake / unrecognized university’ has been the major reason for education discrepancy in Pharma/Biotech/Clinical Research industry.

‘Candidate not residing’, ‘candidate not responding’ and ‘house locked’ are the prominent reasons for discrepancy in address verification in Pharma / Biotech / Clinical Research industry.

EMPLOYMENT VERIFICATION

REFERENCE CHECK

2.08%

Incorrect Tenure Incorrect Remuneration

1.66%

Referee did not Respond

1.00%

Negative Feedback/ Performance Issues

0.91%

Still Active in Organisation

0.91%

Fake / Forged Documents

Referee did not Respond

2.89%

Referee refused to verify

0.48%

Negative Feedback/ Performance Issues

0.24%

‘Referee did not respond’ was the only prominent reason for reference check discrepancy in Pharma / Biotech / Clinical Research industry.

0.50%

‘Incorrect tenure of one of the previous employments’ has been the major reason for employment discrepancy in Pharma/ Biotech / Clinical Research industry. Trends & Insights 2016 | 87

5.8.4 Gender-Wise Discrepancy Trend from FY14 to FY16 Figure 5.8.4: Gender-Wise Discrepancy Trend from FY14 to FY16 FY14

23.30%

FY15

23.50%

20.14%

FY16

23.16%

7.54%

10.97%

Figure 5.8.4 shows overall discrepancy for analysed male and female candidates across Pharma / Biotech / Clinical Research Industry from FY14 to FY16. In line with the overall trend, male candidates are still more likely to produce false information compared with female candidates.

5.8.5 Age Group-Wise Discrepancy Trend from FY14 to FY16

45-49

8.82% 16.25% 36.84%

25.33%

20.93% 16.52% 15.97%

16.77% 23.01%

15.75%

11.28%

9.45%

7.25%

40-44

FY2014 FY2015 FY2016

FY2014 FY2015 FY2016 19.44% 14.19%

11.29% 16.62%

35-39

FY2014 FY2015 FY2016

30-34

25-29

7.03%

FY2014 FY2015 FY2016

20-24

FY2014 FY2015 FY2016

Figure 5.8.5: Age Group-Wise Discrepancy Trend from FY14 to FY16

Figure 5.8.5 shows overall discrepancy for all analysed age groups across Pharma / Biotech / Clinical Research Industry from FY 2014 to FY 2016. The discrepancies have decreased compared with last year; the highest discrepancy being in age group 35-39.

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CONCLUSION

ackground screening has become an essential need across industries, right from giants like IT, Telecom and Financial services to rapidly growing industries like on-demand companies. Rapid adoption of technology and intelligent data analytics is powering the increased use of background screening services. While processes like aptitude test and interview assess the candidateâ&#x20AC;&#x2122;s abilities and competencies, background screening assesses their integrity and genuineness. Comprehensive background verification provides insights into inconsistencies, gaps and mismatches, thereby, influencing and driving the decision of whether or not to trust a relationship / alliance. This report takes a look at the discrepancies across different industries based on cases conducted, checks executed, gender and age of candidates analysed etc. An industry-wise analysis has also been presented. Four most commonly used checks across different industries, viz. Employment, Education, Reference and Address checks have been considered for analysis. Other checks such as identity check, drug abuse check etc. are discussed briefly. Though still not significant in volume terms, other checks category is increasingly being used across industries. We expect identity check to grow in volume in the coming years.

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The key highlights from this Annual Trend Report can be summarized as under: 1. Overall Discrepancy levels have declined from 14.13% in FY 2014 to 10.29% in FY 2016, indicating that adoption of background screening practices across industries has brought in awareness and discipline across candidates. This has also helped improve the quality of hires and decrease recruitment costs and turnaround time for recruitment and on-boarding. 2. Among all the checks conducted through the last financial year, employment verification witnessed the maximum discrepancy followed by address verification, reference check and education verification respectively. 3. During Employment Verification, majority of the candidates have been found faking tenure. 4. During Education Verification, majority of the candidates have been found faking / forging educational qualification documents. 5. During Address Verification, majority of the houses have been found locked and hence, were not verified.

6. During Reference Verification, majority of the referees did not respond to verification calls. 7. The Financial Services Industry (including both banking and insurance) was found to have the maximum discrepancy across all industries analysed. This was followed by the upcoming and growing Internet / e-Commerce / Dotcom industry

Glossary of Terms 1. CASE(S): A single case refers to an individual employee application that AuthBridge verifies. A case may comprise of a single or multiple checks. 2. CHECK(S) Verification of credential of a candidate is known as a Check. Check maybe of many types such as Employment, Education, and Address etc. 3. DISCREPANCY A discrepancy is a mismatch between information stated by a job applicant in a resume / job application and the actual information.

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ABOUT AUTHBRIDGE

uthBridge, one of India’s Leading Background Screening and Risk Assessment Companies has been in the business since 2005. With clients across a broad spectrum of businesses including Fortune 500 companies, AuthBridge has a delivery capability in 140 countries in compliance with International Standards of Quality and Compliance. AuthBridge has emerged as one of the pioneers in pre-employment screening industry and recognized as the preferred choice for clients for employment verification across industries and levels. Over last 11 years, AuthBridge has set the industry benchmark with technological expertise, deep process

11 YEARS OF SUCCESS

CONDUCTED

10 M + CHECKS

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knowledge and innovation. AuthBridge is ISO 9001:2008 certified for quality and ISO / IEC 27001:2013 compliant for information security. An empanelled background check company with National Skill Registry (a NASSCOM initiative), AuthBridge is also a member of NAPBS APAC. AuthBridge, with its robust partnerships, has been recognized with many accreditations. In 2012, Cornell University published a case study on AuthBridge Process “Innovate HR operations in India” after studying the automated background verification process at Authbridge

500+

20+

CLIENTS

SECTORS SERVED

2 M+

REPORTS PUBLISHED

Products and Services AuthBridge uses cutting edge technology and intelligent data analytics to provide quick, efficient and accurate background screening services. With these services and products, AuthBridge aims to ensure organizations are safe and compliant. A market innovator delivering world class services, AuthBridge takes pride in its in-depth knowledge of the processes and its intelligent use of technology to cater to the specific, customized needs of its clients. Our spectrum of offerings include: 1) AuthBridgeOnline™: An e-commerce platform for ordering background checks across 140+ countries across the globe. Visit www.authbridgeonline.com to know more. 2) AuthLead™: Verifying business executives and top brass needs a focused approach compared with other employees. AuthLead offers 360 degree background verification for your top brass. Visit www.authbridge.com to know more. 3) WorkAttest™: This online exit employees’ data repository is the ultimate solution for secure, accurate and instant employment verification. Visit www.workattest.com to know more. 4) IndiaVerify™: An online service for verification of Tenants, Domestic Help, Drivers, Marriage Partners, IndiaVerify is the B2C service by AuthBridge. Individuals can also get Self-Verification conducted through this portal. Visit www.indiaverify.com to know more. We do not just screen individuals but also entities. Verification solutions for entities include: • • • •

Vendor Due Diligence Investment Risk Due Diligence Post Transaction Risk Watch Know Your Customer (KYC) Services

To know more, please visit us at www.authbridge.com or write to us at communication@authbridge.com

Trends & Insights 2016 | 93

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