Can predictive analytics help arrest insurance fraud

Can Predictive Analytics Help Arrest Insurance Fraud?

May 2014 BLOG POST

Impact of Fraud on the Insurance Industry In the US, fraud is estimated to account for about 10% of the total property and casualty insurance claims each year; the figure can vary based on the line of business, economic conditions and other factors. During the last five years, property and casualty insurance fraud amounted to about USD30-40 Bn each year, according to the Insurance Information Institute.

Top five questionable claims, by insurance type, first three quarters (2011-2013) Rank

Type of insurance

1

Workers compensation

2

2011

2012

2013

First three quarters, 2011-2012 (percent change)

First three quarters, 2012-2013 (percent change)

3,591

4,470

4,662

24

4

Vehicle

26,598

31,297

32,698

18

4

3

Casualty

40,500

44,248

44,476

9

1

4

Property

11,880

16,723

16,720

41

-3

5

Commercial

1,426

1,782

1,556

25

-13

Total questionable claims

74,944

87,684

93,053

17

6

Invariably, the customers have to borne this by paying additional premiums to make up for the theft.

The National Insurance Crime Bureau (NICB) indicates that insurance fraud is costing policyholders an estimated USD200-300 a year in additional premiums. Additionally, the NICB reports that questionable insurance claims increased 6% in the US in the first half of 2013 compared with the previous year.

Various Initiatives to Fight off Fraud Hit by declining net income coupled with economic slowdown, the US insurance industry has taken various measures to identify and reduce fraud. It is helped by the government who has stepped up efforts by introducing new regulation and centralized fraud bureaus. Insurance companies too have added their bit to the efforts by establishing special investigative units (SIUs) to detect and prevent fraud in addition to employing computer-based tools and enhanced back-end review processes with automated search. Still, the problem is not controlled, and in recent years, has grown to a significant proportion.

Can Predictive Analytics Help Arrest Insurance Fraud

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Cost and brand make insurers wary to face Fraud Many insurance companies think that detection of claims fraud is too expensive. Moreover, as the insurance industry is increasingly focusing on customer satisfaction, insurers are too hesitant to slow down claims processing in order to provide room to investigate the potential fraud case. The situation could get worse if they mistakenly target a legitimate claim and an honest policyholder for investigation.

This could negatively impact the company’s brand.

Hence, they simply accept a certain amount of fraud loss as a standard cost of doing business.

Implementation of an effective fraud detection program could result in various benefits: • Improved service levels and enhanced efficient claims processing system • Positive customer feedback and increased customer loyalty • Ability to fight fraud from simple individual incidents to complex organized cases of multiple frauds

However, companies cannot afford to ignore this issue any longer and need to adopt advanced technology-based tools to fight the fraud. Sutherland Research experts believe that claims fraud detection is the area where insurance companies would increase investments in the next three to five years. The growth rate of insurers investing in claims fraud detection would primarily depend on the level of understanding the importance of analyzing data using advanced technologies. Today, companies are leveraging a growing number of platforms including social media analysis, text mining and analytics to detect patterns of fraud as early as possible.

Combating Insurance Fraud via Predictive Analytics Predictive modeling approach has been widely used by insurers in claims fraud detection. In recent years, many insurers have turned to predictive modeling processes to reduce the time required as well as manual efforts to verify accounts for fraud. In this approach, quantitative analysts use data-mining tools to build programs that produce fraud propensity scores. In a program, adjusters simply enter data, and claims are automatically scored for their likelihood to be fraudulent and made available for review.

These solutions are also helpful for insurers to take corrective actions based on portfolio segmentation, which is important to understand different levels of profitability with different portfolio segments.

Predictive modeling is more accurate than other fraud detection methods as in this method information can be collected and cross-referenced from a variety of sources. This diversity of resources provides a better balance of data than the more labor-intensive flag system. Although it is not new to fraud prevention, when combined with other analytical tools as part of a concerted global effort, predictive modeling can be highly effective.

Can Predictive Analytics Help Arrest Insurance Fraud

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