The 10 Most Trusted Fraud Detection Solution Providers, 2019

The 10 Most Trusted Fraud Detection Solution Providers, 2019

Vol. 10 Issue. 03, 2019

Mentor’s Standpoint Beyond Automation:ai Powered Autonomous Factories Of The Future Knowledge Keys Leadership Skills Essential For Organizational Growth Industry Intel Eminence at the edge

+ Secured Vision Key POS Trends Reshaping the Retail Sector Tech Infrastructure Disruptive Technology and Changing Trends Inuencing Business Mark Gazit CEO ThetaRay

ThetaRay Rendering Exemplary Fraud Detection Services

E Man vs Machine Who is More Eective Detecting a Fraud?

S

ince its inception Machine Learning has played an instrumental role in solving few of the critical business problems, which include accurate medical diagnosis, email spam, etc. Thanks to the increasing processing power, advancement in Big Data and various statistical modeling, the adoption rate of Machine Learning has grown by leaps and bounds.

With the increased adoption rate of ML, the number of payment channels, and transactions have also increased, which have made Fraud Management a painful process for the Banking, Insurance and Commerce Industries.

Additionally, machine learning models tend to amplify some elements of risk. However, most of the banks that operate under stringent regulatory requirements have the needed framework in places to minimize the risk associated with the traditional models.

By keeping these in mind, banks nowadays are proceeding carefully, and restricting the use of ML models to various low-risk applications including digital marketing, which is quite fair due the potential regulatory, ďŹ nancial and reputational impacts that can happen from frauds.

Also as humans, we have the tendency to find a face behind any decision, whom we can ask questions and understand how decisions are being made. On the other hand, we often have hard time to trust the decision of an algorithm mainly because of the less transparency. Also before using any new algorithm, organizations have to review and check them for any errors. Additionally, organizations also have to make sure that the data which are being fed is completely ethical, immune to any manipulation and accurate. So, there’re many conditions that one needs to keep in mind before trusting the results.

Another point is, when people manage algorithms, they have to think very carefully regarding how they are setting up their risk assessment levels. If it is not sensitive enough then it can create a massive loophole, and risk will increase. Otherwise if the alarm is too sensitive then it will raise too many alarms, which will lead to manual sorting through all the risks on a case by case basis, reducing the effectiveness of the algorithm.

In the long run ML is going to be an inseparable partner for humans. If we misuse it, then it can be disastrous. If we use it right, it can be our partner.

So, with great enthusiasm Insights Success has shortlisted The 10 Most Trusted Fraud Detection Solution Providers, 2019, who are working round the clock to help is clients detect fraud, faster!

Featuring our Cover Story is ThetaRay, with the goal of transforming the way the world benefits from data. The organization is dedicated to helping clients at large financial organizations and Industrial Internet of Things (IIoT), companies that have become more resilient against threats.

Also, while flipping the pages don’t forget to go through the articles and CXOs written by our in-house editorial team and industry experts respectively.

Kaustav Roy

TABLE OF

CONTENTS

ThetaRay Rendering Exemplary Fraud Detection Services

08 Cover Story

Fraud Detector in BFSI Market

Emre Sayin

CEO

Making KYC an Easy Affair

22

Victor Fredung

CEO

30

Mentor’s Standpoint

Knowledge Keys

BEYOND AUTOMATION: AI powered Autonomous Factories of the Future

Leadership skills essential for organizational growth

Emre Sayin CEO

18

Industry Intel

Eminence at the edge

Emre Sayin CEO

34

Victor Fredung CEO

26

Secured Vision

Tech Infrastructure

Key POS Trends Reshaping the Retail Sector

Disruptive Technology and Changing Trends Influencing Business

20 38

Editor-in-Chief Pooja M. Bansal Managing Editor Anish Miller

Executive Editor

Assistant Editors

Kaustav Roy

Jenny Fernandes Rohit Chaturvedi

Visualizer

Art & Design Director

Associate Designer

David King

Amol Kamble

Sanket Zirpe

Senior Sales Manager Co-designer

Co-designer Savita Pandav

Business Development Manager

Kshitij S

Peter Collins

Marketing Manager

Sales Executives

John Matthew

David, Kevin, Mark, Sagar

Technical Head

Business Development Executives

Jacob Smile

Steve, Joe, Alan, Sanket

Technical Specialist Aditya

Digital Marketing Manager Marry D'Souza

SME-SMO Executive Prashant Chevale

Research Analyst Frank Adams

Database Management Stella Andrew

Circulation Manager Robert Brown

Technology Consultant David Stokes

sales@insightssuccess.com October , 2019

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The 10 Most Trusted Fraud Detection Solution Providers 2019

Rendering Exemplary Fraud Detection Services

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Our advanced platform for inancial crime detection provides focus on irregular activities and prioritizes transaction patterns that require further examination.

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Fighting Financial Crime

Mark Gazit CEO ThetaRay

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ThetaRay protects banks against inancial, operational and reputational damage by immediately detecting sophisticated fraud, money laundering and ATM hacking threats.

Below are the highlights of the interview conducted between Mark and Insights Success: Give a brief overview of the company and its vision.

ThetaRay was founded in 2013 by acclaimed mathema cians Prof. Ronald Coifman and Prof. Amir

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I

n an interview with 'Insights Success', Mark Gazit, CEO of ThetaRay shares insights on how the company helps clients at large financial organiza ons, cyber security divisions and cri cal infrastructure become more resilient and seize opportuni es. Also, he broadly discusses the company's core competencies and the services it offers.

Averbuch with the goal of transforming the way the world benefits from data. We are dedicated to helping clients at large financial organiza ons and Industrial Internet of Things (IIoT), companies that have become more resilient against threats.

Our advanced analy cal solu ons are based on AI and machine learning technologies, built on proprietary algorithms developed by Coifman and Averbuch throughout 15 years of research. Their breakthrough technology, hyper-dimensional mul -domain big data analy cs, has the dis nc ve ability to fuse and analyze massive amounts of heterogeneous data from diverse sources like network traffic, financial transac ons and

database records. This holis c, all-seeing technology provides automa c, unsupervised, real me discovery of the "unknown unknowns" -- threats and risks that are not detected by exis ng rule-based solu ons.

ThetaRay lets math discover meaning in the data without making any assump ons. It has no need for any seman c or contextual understanding, predetermined pa erns, rules or other known elements. The technology operates with unprecedented speed, accuracy and scale, enabling clients to manage risk, detect money laundering schemes, uncover fraud, expose bad loans, iden fy ATM hacking, and more. With offices in Israel, NY, London and Singapore, ThetaRay is privately backed and has raised over $65M in investment. The company is led by cyber expert CEO Mark Gazit.

How do you diversify your products and solu ons in order to benefit your customers?

Our pla orm is industry-agnos c, so it can be used in any sector. However, we currently focus on the financial and IIoT sectors. Our three financial services solu ons are: An -Money Laundering: Today's criminal organiza ons are intelligent enough to bypass exis ng AML rules and knowledge. When they launder money, they use small transac ons that look legi mate. However, ThetaRay iden fies anomalous pa erns of behavior that suggest money laundering is taking place, allowing banks to intercept the crime in its ini al phases.

Fraud detec on: With the transi on to digital, banks must manage a acks exploi ng all their new and exis ng channels and products. With unsupervised machine learning, they can detect fraud without predefined thresholds or assump ons.

ATM Security: We are seeing organized a acks on ATM control networks. Instead of crea ng skimming devices and trying to fool the machines themselves, these criminals are engaging in massive a acks that penetrate the management and control networks of ATMs and make them distribute large amounts of money. When this occurs, it is a large-scale event that is almost

impossible to iden fy in real- me. However, ThetaRay can detect it in its earliest stages.

Describe the experiences, achievements or lessons learnt that have shaped the journey of ThetaRay. We ini ally launched the company as a cybersecurity provider for cri cal infrastructure but, instead of hun ng viruses, our technology looked for slight anomalies in everyday processes. This allowed it to detect both cyber-a acks and general equipment malfunc ons. We later realized that financial organiza ons face similar risks as cri cal infrastructure, but with greater financial losses at stake. As a result, we shi ed our focus to the financial sector.

What are the evident challenges in the Fraud detec on Solu ons industry?

The key issue is that the banking industry's tradi onal rule based fraud detec on methods do not work anymore, because most fraud now takes place online and most criminals know the rule thresholds. Add to that the fact that financial organiza ons are genera ng massive amounts of data, and you can understand why they might feel helpless against new types of threats.

Describe the significance of machine learning in fraud detec on space.

Machine learning is an important trend in fraud detec on, since tradi onal solu ons are incapable of detec ng today's complex threats. Even the industry's regulatory bodies have begun sugges ng that banks use new technologies such as AI. However, not all machine learning solu ons are created equal, and we are seeing industry confusion over three types: Supervised machine learning: This is what most of the vendors are using, and it's essen ally the same thing as the old rules-based systems: you tell the machine what to look for and it finds it. Unfortunately, new and unfamiliar schemes are missed en rely.

Unsupervised machine learning: A few companies are offering unsupervised machine learning solu ons, which can detect unknown threats based on anomalous behavior. Unfortunately, this all takes place in a 'black box,’ so banks cannot submit SARs based on these conclusions.

Intui ve machine learning: This is what we call our form of AI, which is unsupervised yet transparent. It is the only machine learning-based solu on on the market that completely explains every decision it makes, and thus enables banks to submit suspicious ac vity reports (SARs), which is a cri cal point for regulators.

What are the current trends that are driving the industry?

We see two key trends driving AML and fraud detec on today: Ongoing and escala ng penal es: Even though banks are using fraud detec on and an -money laundering solu ons, they con nue to get fined – and even indicted – for AML viola ons. A big part of the reason for this is because criminal enterprises are using extremely sophis cated techniques to funnel and cleanse money that is used to finance human trafficking, drug trafficking, terror financing and other illegal opera ons.

These groups know the rules and thresholds that banks' detec on systems look for, and subvert them in some very clever ways.

big data tackling the most difficult challenges. Originally founded out of Israel, ThetaRay has seen tremendous demand for its solu ons on a global scale. Thus, ThetaRay currently has four offices in the US, Israel, UK & Singapore and is providing its solu ons and support for each region. Today we are dedicated to helping clients at great financial ins tu ons make giant strides in managing risk. Detec ng money laundering schemes, uncovering fraud, exposing loans that are likely to fail, and revealing valuable new customers whose credit scores only tell part of the story. But on the horizon, we see almost limitless poten al: to support professionals of every kind, in every industry, make their organiza ons as resilient as these mes require, safeguarding assets, recovering from setbacks, and capturing future growth amid con nuous and wrenching change. Ÿ

We have four key differen ators: Ÿ

Full transparency: ThetaRay's machine learning algorithms were inten onally designed from their incep on to produce output that is fully supported by easily accessible forensic evidence. To understand why ThetaRay has iden fied an ac vity or customer as suspicious, users can click through the full data lineage and see every single transforma on that was made to the raw data -- including how each anomaly was iden fied through sta s cal comparisons to preestablished 'normal behavior.’

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High detec on rate: Because our technology analyzes massive amounts of data, our detec on rate is 5-10x greater than compe ng solu ons.

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Low false posi ves: The biggest problem with today's threat detec on systems is their staggering false posi ve rates. In some cases, 99.5% of the alarms generated are false. This creates 'detec on fa gue' for companies, making it easy for them to overlook real a acks when they take place. Our false posi ve rate is 10 to 100 mes lower than that of compe ve solu ons.

New regulatory support for AI: The Financial Crimes Enforcement Network (FinCEN) and all four U.S. Federal regulatory bodies recently released a Joint Statement on Innova ve Efforts to Combat Money Laundering and Terrorist Financing that not only recommends that banks try AI-based approaches to AML; it essen ally guarantees financial ins tu ons that they won't face regulatory ac on if AI finds money laundering events that their exis ng systems were unable to detect!

Where does ThetaRay envision itself in the long run and/or what are its future goals?

ThetaRay's groundbreaking unsupervised machine learning technology is the result of over two decades of academic research led by world-renowned mathema cians, Professor Ronald Coifman (Yale University) and Professor Amir Averbuch (Tel Aviv University). Their patented approach has been designed from the ground up to automa cally detect meaningful anomalies from massive data volumes with unprecedented accuracy and performance. ThetaRay was launched to transform how the world benefits from

Considering the rising number of fraud detec on solu on providers, how does ThetaRay stand out from its compe tors?

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Always up to date: Our system doesn't rely on any rules, so it's always up to date. It educates itself via deep machine learning, and updates automa cally.

Clientele Assessments A mul na onal bank that had previously failed to detect its exposure to the Russian Laundromat money-laundering scheme contracted ThetaRay to conduct a review of its correspondent banking ac vity over the previous six years.

The bank was pleasantly surprised by the results of a project they ini ally believed to be "out-ofreach." However, ThetaRay algorithms were able to seamlessly parse through 200-million SWIFT messages, which are notorious for their data quality fric ons, to illuminate the client's correspondent-banking black hole.

Using SWIFT messages alone, ThetaRay uncovered three new money-laundering pa erns contamina ng the bank's business, which they had previously thought they caught all of the money laundering schemes occurring at the bank.

A er-1 bank engaged ThetaRay to analyze over 12 months of data, including 45-million transac ons and over 100,000 business customers, and was jolted by the discovery of five new confirmed money laundering pa erns. While we not only detected unknown events, in this Pilot, ThetaRay's analy cs pla orm detected 100% of the true posi ves that the bank's legacy system detected.

About the CEO Mark Gazit is the founding CEO of ThetaRay and has played a crucial role in growing and guiding the business since its inception. He is one of the top cyber security experts in Israel, with a longstanding reputation dating back to his cyber security service in the Israeli Air Force. Mark is a prominent senior executive with 20 years of experience in Israeli and international high-tech companies. Prior to ThetaRay, he served as Managing Director of Nice Track, which provides software and hardware solutions to government agencies worldwide in the areas of information intelligence and cyber. He was also the Group President & CEO of SkyVision, which he took from the start-up stage to an international company serving over 50 countries worldwide. Mark has held additional pivotal roles in leading companies.

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BEYOND AUTOMATION:

AI powered Autonomous Factories of the Future

R

ise of the industrial robots in 1980s led to a major evolution of Henry Ford’s assembly line concept. Routine tasks that were being handled by factory workers started to be performed by robots with greater accuracy and efficiency. With the spread of device-to-device communication and IoT protocols, robots started taking a serious amount of responsibility from factory workers. This movement was initially led by German high tech firms that branded this shift as the 4th industrial revolution. On the opposite side, there was China, taking advantage of their access to low cost human labour as an alternative to automation. Very recently, a few years ago, China flipped their strategy 180 degrees, becoming the biggest advocate of automation by starting to invest aggressively on buying robots and developing their own robotics know-how led by government initiatives. So the technology won. There is no question about the need for automation anymore and major manufacturing companies, including the ones in China, Germany and rest of the world are racing to automate their industrial processes to increase productivity.

Daghan Cam Co-founder & CEO Ai Build

18 | October 2019

Everything that can be automated will be automated, according to Zuboff’s law.

However, some things are more difficult to be automated than other things.

Those are non-repetitive tasks which require higher level of cognition and ability to adapt into unknown conditions. These tasks can be defined as the last mile in factory automation. Tesla, the electric car manufacturer, was recently criticized by analysts for automating their assembly line more than necessary. The reason of the criticism was the fact that the cost of automation exceeded the cost of a human led assembly

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Mentor’s Standpoint line. This may be a valid argument for today but the solution should be found in increasing the bar for commercially viable automation and not by going back to lower levels of automation.

So if we are moving towards full automation and lights-out manufacturing, how can the last mile in factories be automated without overspending and over engineering?

Autonomy seems to be the answer, therefore Artificial Intelligence…

There is a slight but important difference between Automation and Autonomy. Autonomy is the state of being able to make independent decisions in situations that were not experienced before. By definition, autonomous systems are always automated, but the other way around is not always the case. A system may be fully automated but not autonomous.

“Blind Automation” is the term we use for this category.

Systems that are blindly automated are based on hard coded rules, as in expert systems, and they tend to be very difficult to be reconfigured if the conditions or requirements change over time. Autonomous systems, in contrast, generalize the rules for decision making in different scenarios by allowing a higher level of abstraction in their programming.

For example, in the blind automation scenario, a robot may be programmed to go to coordinates x, y, z in order to pick up an object. This will work without problems as long as the object is found precisely at coordinates x, y, z.

However, if for any reason the object is positioned at a slightly different location, the robot will still go to x, y, z, fail to pick the object up and continue executing the rest of its commands without noticing this problem. Eventually every step subsequent to this failure will also fail.

A better strategy for performing the same task, is using a robot with vision

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sensors, and programming it to go to the object’s position (as a variable) to pick it up. Regardless of where the object is positioned, robot will localize it using its sensors and successfully pick it up. This difference between the two programming paradigms; “go to x, y, z” and “go to an object’s position” makes a big difference in making the system less prone to making mistakes in unexpected conditions which is crucial for last mile factory automation.

About the Author Daghan Cam is the Co-founder and CEO of Ai Build, a London based startup developing Artificial Intelligence and Robotics technologies for the construction industry. He is also a visiting lecturer at University College London doing research on robotic fabrication, large scale 3d printing and parallel algorithms with GPU computing.

His work focuses on developing intelligence for automating complex tasks in design and manufacturing by using computer vision and machine learning techniques.

In this scenario where robots make autonomous decisions by using realtime sensor data, a feedback loop is established between physical and digital environments. The constant flow of information from physical to digital and vice versa create immense amounts of structured data which is the fuel for AI powered autonomous factories of the future. Such actionmeasurement-action strategies allow the systems to self-improve over time, creating a data network effect in manufacturing. The more one process is executed, the more efficient it will get. This allows super-human performance in almost every domain given enough data.

Robots making hundreds of decisions every second and receiving hundreds of measurements from the environment and from other devices also require massive computation and storage capabilities. Despite the popularity of cloud computing in most applications today, a decentralized compute power is necessary for most robotic applications and for IoT devices in general. The throughput between the edge and the cloud through internet connectivity is simply not enough to process all data generated by sensors and make sensible decisions in realtime. So edge computing, or fog computing - dedicated servers on premise as a layer between individual devices and the cloud - will likely be the norm in autonomous factories of the future unless we experience a breakthrough in science that leads to infinitely fast and reliable internet connectivity. However unlike compute power, storage of important data, that is filtered on the edge, should be centralized in order to achieve the best decision making models at every location.

As a summary, a few conclusions and predictions about the future are: Ÿ

We are moving towards an autonomous black-box factory model where the factory robots are operated by themselves without human intervention.

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These factories will be powered by the data that is generated by themselves and their productivity will increase over time exceeding human performance in every process involved in manufacturing.

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Factories will compute on-premise / on-edge and they will push important data to cloud for benefiting from a shared pool of data with other factories.

www.ai-build.com

October 2019 | 19

POS Trends Reshaping the Reatil Sector I n recent times, the retail industry hasn’t seen a more exciting invention since the invention of cash register.

With new and innovative technologies helping shape both online and offline experiences for consumers, the landscape is continuously changing in a way which was unimaginable even few years back. The best part is that there seems to be no end of the innovation, which only influencing the purchase decision of the consumers.

Nowadays the main focus of retailers is to create a safe, engaging, and unique shopping experience for its consumers, it’s very important for the retailers to understand the importance of Big Data and in-store analytics and adapting to the cloud. With the retail industry at the verge of massive transformation, we are listing out few key trends that everyone needs to know to be successful in the ecosystem that is transforming quickly.

Multi-system Integration Multi-system integration with various applications gets the utmost priority from top retailers. Most of the retailers list out POS integration with other applications as a key priority alongside the implementation of dynamic marketing content through mobile devices. This is mostly due to the retailer’s interest to store all the customer information and purchase history in a database, which is completely centralized that could be easily integrated with multiple applications.

However, in order to do that, a retailer needs to use an ERP database that can handle all these.

Speed People always look for quick solutions for everything. A clock starts ticking the moment a customer enters, no matter how good the product is, if the process is slow and the attention to details are missing, then customers will leave

20 | October 2019

disappointed. As a retailer, one cannot please everyone, but with a modern and efficient POS, the service can be improved. A modern POS simplifies the communication between various departments and can save a lot of time for both the retailer and the customer respectively.

Managing Stocks Keeping and managing inventory is a nightmare for most of the retailers, and it’s quite natural. Managing inventory is a never-ending task and takes a lot of effort, time, and manpower. However, it is quite important to manage inventories when it comes to long-time survival. An efficient POS system always makes the process of managing the inventory much easier. The best part of a POS is, one can monitor the status of stocked items, shipped products, and new orders anytime. This is a huge time saver for a cumbersome and a tedious process, and eventually helps retailers to focus on other important aspects of running the business.

Customized Experience With POS systems, retailers just need to provide personalization that scoops out every shopper. Every passing year, retailers are adapting to personalized technology solutions that allow an interactive user experience. Thanks to the emergence of all new mobile POS technology, now retailers can offer its customers more choices to accommodate their shopping habits by letting them to complete transactions anywhere in the store. Now with the invention of improved POS marketers and customer service teams can contact the buyer at each point of their purchase decision. With so much data retailers and consumers can have better customer service, quicker payment processes and access to better offers and real-time personalization.

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Secured Vision Promotions and Marketing at its Best Nowadays with the advent of digital technology, marketing involves maintaining a digital presence as well. A POS can integrate all the advertised offers with transactions, making it easier to keep track of all the campaigns. Additionally, it can integrate with CRM and track customer behavior. When an offer gets popular among the masses, then the retailer will see it in his transaction data.

Usage of Big Data analytics In order to compete with e-commerce, retailers are now taking the help of Big-Data and in store analytics just to have a better idea about what’s happening inside the store.

Big-Data analytics helps retailers to track how frequently a specific item moves from shelf to shopping cart allows retailers to know the trends that are dominant in the market.

Analytics helps the retail industry in a big way to better understand consumer purchase pattern and behaviors.

Keeping Track of Employees To run a business smoothly a retailer, need few people. A POS system enables to manage them with great accuracy.

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With a Point of Sale system in place, employees can sign on or off easily and the system will automatically log their work hours and break hours.

Security Above all, a POS system offers great security protections that help keeping customer data safe. Retail stores and businesses are always prime targets for Cyber Criminals, and a data breach is not good for a business. So, by using standard encryption and firewall, businesses can be secured from cyber-attacks and customers can swipe their cards with a peace of mind.

So, here we have listed out few of the POS trends that will shape the future of the retail industry. As we look ahead, these trends will be on focus for both retailers and customers. The main advantage of an advanced POS system is greater efficiency and optimization, it links all the departments together which eventually allows to have better control over the inventory, better profitability, and to manage processes in an efficient way.

October 2019 | 21

The 10 Most Trusted Fraud Detection Solution Providers 2019

Fcase

I

n the digital age, financial corrup on against banks, building socie es, credit card holders, and other financial services businesses are accelera ng rapidly.

Today's financial services industry is challenged with increasingly innova ve ways of commi ng fraud and cybercrime.

From malicious malware to sophis cated phishing schemes, they are under constant mul -channel threat. While there are a number of fraud detec on so ware solu ons available for individual internet banking, mobile banking, and credit card pla orms, each has its own case management interface, the majority of which fail to meet the needs of a financial service provider.

So, born FCASE, which can improve the opera onal efficiency by 300% and close fraud cases ten mes faster. The company helps Fraud Opera on Centers to orchestrate their "Resources", "Data" and "Fraud Systems".

FCase's next-genera on technologies integrate different data pla orms, collec ng informa on from mul ples fraud detec on sources to manage fraudulent ac vity in real- me, using adap ve analy c. The so ware provides a cross-channel fraud analysis which empowers fraud-screening teams to make quicker decisions based on reliable informa on, overcome data fric on, and achieves the velocity of DataOps demanded by modern financial ins tu ons. The company's

22 | October 2019

Fraud Detector in BFSI Market

highly skilled team has extensive exper se in fraud detec on and preven on technologies which help banks, building socie es, and credit companies transform their fraud management capabili es.

About FCase Founded in 2007, FCase is situated in the city of London. With a company size of more than 50 employees, it is an Informa on Technology & Services private held industry. The company is an end-to-end Fraud Orchestrator which drives fraud management systems from basic, standalone detec on to an enterprise-focused approach. This holis c view of fraud data allows standardizing fraud case interac on, fraud management processes, governance models, and performance and quality indicators.

The company's next-genera on technologies, aggregates dis nct data pla orms, collec ng informa on from mul ple fraud detec on sources to manage fraudulent ac vity in real- me by using adap ve analy cs. The organiza on spans the en re financial crime, risk compliance, and customer care systems, centralizing alerts and events into one enterprise-wide inves ga on pla orm for all your fraud inves ga on, and fraud compliance repor ng needs. The industry consolidates mul -channel fraud data, simplifies fraud management, centralizes data analy cs, and significantly improves efficiency.

Fraud- Global Problem Fraud is a global nuisance, and it has

been around since the dawn of commerce. The earliest recorded case of fraud goes way back to 300 BC, a Greek merchant named Hegestratos took out a massive insurance policy on a shipment of cargo, corn to be exact.

Hegestratos a empted to sink his boat, sell the corn, and keep the insurance proceeds but to no avail. In the end, the plan fell through, becoming the first fraud example on record. From this incident, fraudsters have only evolved in their methods and schemes to defraud the global financial system. It robs from the financial ins tu ons; adversely impac ng merchants, banks, insurance companies, and everyday individuals.

Current Challenges to FCase A few challenges the financial services industry is facing when it comes to increased digital fraud includes mul channel banking op ons, mobile dominant customers, and synthe c iden ty the .

Mul -Channel Banking- Mul -channel banking refers to the array of services financial ins tu ons provide their customers to manage their finances.

Mobile is quickly becoming the dominant channel, but customers can also access ATMs, physical branch loca ons, and telephone to service their banking needs. Providing more channels means providing more value for customers, however, unfortunately, it also creates a silo effect where fraudulent ac vi es can be hard to manage across different channels.

Mobile Dominant Customers- Recent

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At FCASE we help Fraud Operation Centers to orchestrate their " "Resources", " And " " Systems.

FDP

Data

Emre Sayin CEO

advantages to fraud orchestra on, which includes: studies are showing banking customers are going mobile more than ever. A report by Fiserv shows that mobile is now the most heavily used banking channel, with customers accessing mobile banking an average of 8.4 mes within a 30-day period. Financial ins tu ons are pushing for more their customers to use mobile banking services as it is by far the most cost-effec ve method. For example, in 2018 a retail bank spent roughly $4 every me a customer calls or visits a physical branch.

Synthe c Iden ty The - Synthe c iden ty the is defined as a type of fraud where criminals parse together real and fake personal informa on to create a new iden ty, which is then used to open fraudulent banking accounts or make fraudulent purchases. This type of fraud has quickly become the fastest growing and hardest to detect a form of iden ty the to date. This the is the most common type of iden ty fraud and is becoming a major source of losses for financial ins tu ons. Financial ins tu ons must be diligent in finding ways to prevent synthe c iden ty the , which is es mated to be the source of 80% of credit card losses in the industry.

Key to Solu onFraud orchestra on can be the answer to the many challenges facing financial ins tu ons in this digital age.

Fraud orchestra on creates a centralized pla orm where fraud ac vity can be viewed across the en re enterprise, no ma er how many banking channels exist. It creates ul mate transparency where bank fraud divisions can view alerts across any channel in real- me. There are several

www.insightssuccess.com

Ÿ

Increases Opera onal Efficiency- A centralized, an -silo fraud management pla orm which improves communica on between banking channels increasing fraud preven on op miza on.

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Creates enterprise wide transparency- Fraud orchestra on creates a mission control where fraud ac vi es are made transparent across the en re enterprise leading to real- me ac on to catch fraud in the act.

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Decreases opera onal costs- No need for addi onal staff or training monitoring countless fraud management systems, fraud orchestra on brings all fraud systems onto one pla orm. Ÿ

Reduces customer fric on- Faster fraud response equals less customer fric on, for financial ins tu ons, it is that simple.

Responsive Leader in Tech Industry To lead the field of fraud detec on requires vision. With one such vision, Emre Sayin is the Founder and CEO of FCase. Finished his educa on from Sabanci University Emre has a vast experience in the field of technology. He has been leader in many organiza ons like Pakolina, Inoven, Abonesepe , Paygilant, Pubinno, and IHS Technology. He leads his management team and makes quick decisions to achieve success. He is a skilled leader in web applica on security, opera ng systems, cryptography, and networking.

October 2019 | 23

Mark rees

Chief Operating Officer

Secucloud GmbH

26 | October 2019

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Knowledge Keys

Leadership skills essential for

organizational growth Trust is the key for sustainable development of organizations

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an one learn leadership? Every winter semester I pull my students through the whole spectrum of “Leadership Theory Parts I and II”: Taylorism; French & Raven; Blanchard; McGregor; Maslow et al. At the end of the semester I have them test their knowledge in a tricky exercise involving a climbing rope and blindfolds. They have to – whilst blindfolded – form, storm, norm and perform as a team and lay the rope (which they are not allowed to let go of) in a predetermined shape on the ground. And yes, they always achieve the objective – one way or another. In contrast I have experienced this exercise as a team member of highly paid and experienced managers and witnessed first-hand the utter failure to even manage a plan, let alone achieve even half of the objective.

Although to be fair, what the managers did achieve (as opposed to the students), was a whopping great conflict of personal differences, with which the rest of the seminar was used to sort out. So what was the difference between these two groups?

A business acquaintance of mine once introduced me to the “power of trust” as a basic concept of leadership. He argued that if there is no trust between the leader and the led, then the possibilities for sustainable leadership are extremely limited and indeed most probably restricted to a short period. Without trust in human relationships there is however a form of leadership and to reference French

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and Raven, it is the positional power of coerciveness and reward – the proverbial carrot and stick. That works for a while as long as the (micro) manager exclusively owns the processes and information as a means of control. I believe in the long run, this strategy fails. No (high performing) employee deliberately hangs around for very long in such a situation. I´ve seen this happen numerous times.

Again considering the group of students and the experienced managers with their climbing ropes and blindfolds, what I perceived was extreme differences in levels of trust. The students – in their seventh semester – were close-nit. They were just entering their final thesis, they have common objectives and after 3 and half years spent on campus, an intrinsic trust to one another. In contrast the managers came from varying business units of a large company and were “thrown” together to improve their leadership skills and develop as a leadership group within the company. Each manager had his (the group was all male) own agenda and personal career objectives etc. Each was competing for power and influence before the board. Instead of collaborating to achieve the climbing rope objective, all the micropolitics came out in the exercise and trashed any trust that might have been there at the outset. The evening log fire at the beach was a flop.

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About the Author Mark Rees is the Chief Operating recfO at Secucloud GmbH, Hamburg. In his last position Mark Rees was Managing Director of E-POST Development GmbH in Berlin, a subsidiary of Deutsche Post. There, he led an international team comprised of several hundred employees in agile DevOps development teams, and was

Trust as a subject is rationally difficult to grasp. It is one of those things we intuitively feel as being present (or not) in human relationships. To get a grip on the term, let us start with Peter Drucker´s definition of management “turning resources into production”. Now consider that what leadership does in a company is to apply strategies (resources) to achieve objectives (production). On the one hand the objectives should be such that they inspire people, bringing out their best qualities both in skills and collaboration. Secondly and as a rule, it is the leader who drives the strategy in order to achieve those objectives and this is the key area where trust within the team and across teams can emerge. It is the means utilized to achieve the ends that define so much about an enterprise.

There are of course coercive means, non-compliant means, even illegal means etc., thus the old excuse for bad behavior “the ends justify the means”. In such an environment sub groups form, secrecy becomes endemic and there can only be mistrust and suspicion between people.

responsible for employee direction, as well as the entire budget in the areas of IT security, DevOps, quality assurance, operations and user experience. In his work as COO at Secucloud, Rees applies his expertise in agile leadership and his many years of experience in interdisciplinary IT organizations in the Media and IT security industries.

Approaching the skill of leadership whith consideration of social and ecological factors allows decisions to be made under the reflection for example, of waste reduction, for the good of the many, for sustainable growth etc. In utilizing social and ecological factors in decision-making processes, our unique human form of emotional intelligence is an influential force that significantly influences behavior both of oneself as a leader, and as a member of the collective (the business unit being led). Over time common values and understanding emerge in the group, which in turn develop (forms and storms) and fortifies the culture (the norms), thus producing the high performance unit that every person blessed with the opportunity to lead can aspire to. If you get there it’s the best job in the world!

Trust is a human trait that is available to us all. You can´t simply buy it by attending a leadership seminar, doing an MBA or reading a book. Trust has to be earned and shared unconditionally as a gift. It can be rejected, withheld, it is breakable and can be destroyed in a second. Yet for all its non-tangibility and fragility, it has more power to achieve than anything else in the leadership toolbox.

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The 10 Most Trusted Fraud Detection Solution Providers 2019

Making KYC an Easy Affair

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hu i Pro, a global AI-based iden ty verifica on service provider, delivers seamless KYC/AML solu ons to its diverse clientele. With clients from every corner of the world, it offers a range of services like document verifica on, consent verifica on, ID verifica on, face verifica on, address verifica on, and supports 150+ languages. It provides quick results with 98.67% accuracy using a hybrid of Human Intelligence and Ar ficial Intelligence.

Shu i Pro envisions to become the leader in the global iden ty verifica on industry and contribute towards making cyberspaces fraudfree. It provides low-priced highquality services and con nuously updates its databases and services to achieve its long-term objec ves.

Programs that are Constantly Being Updated Shu i Pro remains updated with changes in global due diligence, KYC/AML, and data protec on regula ons. It stays in touch with regulators to deal with unprecedented issues in global compliance. Its exhaus ve databases are updated every 15 minutes, with global sanc on lists, watch lists, PEPs lists, etc.

Moreover, Shu i Pro constantly improves its services through technological advancements. For instance, in 2019, it enhanced its API integra on by providing Auto Code

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The Great Journey The company gained global acclaim in a short period, increasing the value of the company manifolds.

Generator for swi and seamless integra on.

The Skipper Victor Fredung, the CEO of Shu i Pro, is a fintech innovator with significant experience in the industry. He knows the financial domain inside out.

The organiza on flourished under his supervision and became a remarkable name in iden ty verifica on and fraud preven on. Victor enhanced the customer base of Shu i Pro and developed a team of experienced and dedicated professionals.

Shu i Pro prac cally bridged the gaps between global economies by serving businesses in developed countries like the USA and un-recognized countries, such as Sealand. It served clients by verifying people in more than 230 countries and territories within two years of its founda on.

Technological advancements and product enhancement is a con nuous process at Shu i Pro. In 2018, the company started u lizing OCR technology for data extrac on and expanded its services to AML/PEP screening. The industrial challenges mo vated Shu i Pro to deliver services more vigilantly. By serving a diverse clientele, the organiza on also learned new ways to secure the cyber world. Shu i Pro has been represented at several global expos and developed good rela onships with businesses and global organiza ons.

Tackling Challenges and Preparing for Be er Future According to the organiza on, cybercrime is the biggest challenge, and it aims to provide an excep onal fraud preven on solu on to tackle it.

For Shu i Pro, understanding the regula ons is not enough, and so ware must be enhanced with

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Victor Fredung CEO regard to the latest technologies and techniques used in cybercrime.

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Shufti Pro is a global identity verification and KYC/AML service provider. It uses Hybrid (HI & AI) approach for achieving its vision of becoming a leader in the industry.

“

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Another challenge for the company is the lack of awareness among businesses regarding the need for KYC and AML compliance. It is vital to improve apprehension among the masses regarding the significance of digital KYC/AML compliance and fraud preven on to achieve its long-term goals.

The best part is, Shu i Pro stands out among its compe tors due to its unparalleled services given at compe ve prices. It delivers quality services at more affordable prices as compared to its compe tors. The low price does not affect the quality of Shu i Pro's results, it delivers them in real- me with 98.67% precision.

The Middle East Market and Arabic OCR The Middle East offers a plethora of opportuni es for banks and fintech companies but Arabic is one of the most difficult languages for op cal character recogni on (OCR). But Shu i Pro loves challenges, and provides state of the art Arabic OCR with incredible accuracy. Only a handful of iden ty verifica on services cater to The Middle Eastern Market, and Shu i Pro is among them.

Shu i Pro envisions itself as a leader in the global iden ty verifica on and fraud preven on industry, playing a remarkable role in making cyberspace fraudfree.

It aims at enhancing services to an extent where no compe tor could match its quality. Reducing the me span of verifica on process and securing a bigger share in the global market are its two major goals.

Sa sfied Clientele We have a diverse clientele. Kindly visit the 'Press Release' page on our website Shu iPro(.)com to read the tes monials of our sa sfied clients.

October 2019 | 31

minence E

xecutive summary

Since Internet of Things technology started to gain mainstream traction, multiple platforms, solutions and strategies have been developed. At the moment there are more than 450 ‘platforms’ commercially available.

Yet, realistically speaking, most of these have been designed for a very specific function on out-dated technology and mostly down a vertical application path.

The true power and differentiator in IoT.nxt resides in our full IoT stack capability encompassing the edge and the cloud.

Background Our thinking from the outset has been that we wanted to adopt thinking and develop tech that creates horizontal interoperability between multiple systems and platforms in a technology agnostic manner.

Why? Well, historically, technology companies argued that the best way to quickly create commercial value was to develop a strong vertically integrated application encompassing an ecosystem of partners.

In 2002, it was all about the cloud.

Amazon Web Services was launched and, when OPC Unified Architecture was released in 2006 enabling secure communication between devices, data sources and applications, adoption of IoT began to rise. The early adopters developed their projects with the cloud in mind. The thinking being a simple connected mindset where billions of sensors will be deployed and easily spin up supercomputers at low cost in the cloud to process all of this valuable Big Data… how could they go wrong?

The quickest way to show value was to focus on a vertical and go after it. We have a different view.

During the .com bomb era, people ran around with amazing ideas that they thought would take over the world

Similarly, gateway players have developed powerful gateway technology with a portion that generically aggregates data to the cloud.

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once mass adoption took place. This was followed by an implosion which saw a huge number of concepts, ideas and investments disappear. A similar trend is developing in the adoption of IoT and in digitalisation in general.

Part of the demise of this is because they were too early on the initial curve and either ran out of cash, were unable to build what they said they could, or saw new, sexier, more agile technology drive competitors closer to adoption.

The .com bomb was a rationalisation and a reality for companies and their investors resulting in fortunes being made and lost in the hype. Timing is key in driving Big Tech. If you’re too soon, you are potentially busy developing a concept that will not only age quickly but give competitors plenty to learn from and piggy back off

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Industry Intel

allowing them to develop better tech that is more relevant and value driven. Often a cool idea is exactly that - a cool idea, but without real substance it doesn’t get wide commercial adoption. The commercial viability ultimately sits with the ability of a product to produce ‘real value’, whether quantitative or qualitative.

And then there are the guys who make it.

Amazon, Alibaba, Google. They were unprofitable for a number of years before they started to bear fruit simply because they played the long game. They saw past the hype and created products of real value. They made sure they will be relevant in future economies.

The importance of timing Timing is everything, and tech is hard to time We entered this market at the perfect time. Two years in, our solution is strong and businesses at enterprise level are rallying to adopt Big Data technology. They’re embracing VR, AR, AI, cognitive, algorithmic machine learning technologies as they become a reality.

than 2000 as I think investors have been more calculated; but there certainly will be a correction in the not so distant future. Driving my belief in this is that you need this type of event for eminence to be created. People need to start understanding where the true value lies. The companies that have the ability to lock into this IoT business value proposition and convert that into investor value will survive and will gain eminence. There are a number of great technologies and concepts available but only the ones that are able to truly unlock value will remain.

What sets us apart The IoT.nxt approach has been somewhat different, defying the norm and, to date, it is my firm belief that ours is the only company that has this unique approach. Addressing the problems of interconnectivity from the bottom up, our solution acknowledges the power of the cloud and Big data, but also acknowledges that power is greatly diminished or even nullified if the edge layer is not correctly managed.

Our definition of interoperability and data orchestration is, at times, diluted by platform players claiming to provide the same. They don’t.

CXO 2

As irrelevant solutions are being seeded out, the IoT.nxt approach to the problem of IoT is making us a major contender; cementing our position in the market.

If we look at the solutions currently available, we understand more than most of these ‘platforms’ have all been built in the cloud. Five years ago everything was in the cloud, it is therefore unsurprising that it is still dominating IT discussions.

Anyone who has, up until this point, embarked on an IoT initiative, has probably 1. built a solution that resides in the cloud; 2. leverages the power of the cloud and its ability to centralise and leverage processing power from the supercomputers that exist there; 3. adopted a top down approach incorporating the cloud as the central power behind the application.

The competitive landscape Looking at the IoT industry and where the ‘competition’ and ‘incumbents’ are in the current IoT cycle, it is evident that IoT development are in a perfect bubble that I believe is not far from rationalisation. I think it will be less severe

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The general platform interoperability discussion talks to cloud interoperability. This is a hugely complex play that causes massive headaches for some of the most influential players as they try to fathom how to seamlessly integrate multiple platforms. API’s are the talk of the day, with the current solution to solving this dilemma, but it is simply not sustainable or practical. On a whiteboard it might look great having several platforms integrated via API and then plugging into some ESB via microservices, but I challenge to you to construct all of that and take into consideration the small part all of these guys initially did not deem necessary – the edge.

This methodology is hugely reliant on smart sensor technology that has the ability to push data into the cloud.

There’s a heavy reliance on networks and, as a result, ‘platforms’ are struggling to grapple with edge technology, all the while hopeful that a 5G, no, 20G network will resolve this problem.

At almost all the international conferences we have attended in the last 24 months the major discussion has been Big Data and smart sensors, so most of the more mature platforms have been designed around the premise of them being able to receive data directly from the sensor.

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The problem now is how to talk back to the sensor or machine and, more importantly, how to do this cross platform. An even bigger issue creeping to the forefront of discussions are regarding ecosystems in which near real-time data feeds are crucial.

Yet still, the focus is on the cloud and understandably so, especially if you have invested millions into a technology that is reliant on the cloud.

We do not believe this.

For some time now we’ve been saying that the edge is eating the cloud.

We’re not implying that the cloud will lose relevance. What we’re saying is that a true IoT ecosystem will become less and less reliant on the cloud and, in fact, that ecosystem design will rely heavily on edge capabilities.

A natural oversight, but a crucial detail destined to form an integral part of this industry’s ability to commercialise in the near future. The IoT industry is inhibited by an inability to create interconnectivity and interoperability at the edge.

Retrofit and decrease the barrier to entry and sweat the assets.

Correctly designed and engineered, edge technology enables edge interoperability and, more importantly, the ability to retrofit into legacy systems. Legacy systems, to a large extent, were disregarded, with current players relying on the ‘rip and replace ‘mentality that has governed and, to a degree, plagued the IT industry since the beginning, befuddling brands that have become household names.

This mentality of winner takes all is not congruent with the ideation of a connected world and certainly does not embrace the concept of true scalability.

Having to rip out and replace existing

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technology and infrastructure on your journey towards digitalisation introduces a huge amount of additional complexity, disruption and a cost, all of which makes it a difficult sale to the business, contributing to the slow adoption rate of the 4th Industrial Revolution.

So whilst the ‘big dogs’ are all trying to figure how they can develop and ensure technology lock-in to secure future revenue, they’re contributing towards the mixed message that is being sent out to the market, diluting the value of IoT technology as a tool to unlocking real business value.

Value is a simple exercise for any business leader – Look at expenditure, then ROI. Satisfied? Great. Here’s the next question - is it relevant to my business?

All data is not the answer.

When we enter into discussions with big companies, the issue of legacy investments in technology at the edge comes up without fail. Remember that everyone is selling some type of cloud platform that is going to ‘change the business’, but that cloud engine is reliant on edge data i.e. devices, sensors, machines, protocols, PLCs, SCADAs, CCTV, access control systems - the list goes on and on.

Clients start considering negotiating with each vendor and realising that, much like when our 1000 piece holiday puzzles has 1 missing piece can ruin the picture and make the whole exercise seem futile. It’s the same with many of the algorithms and predictive applications - the true power of these platforms lie in their ability to provide companies with insights. For this they are 100% dependent on having the correct, filtered, aggregated, curated, secure, real-time data from the edge, and they need all the pieces of the data puzzle to build the Big Data picture.

In every environment, on every piece of the puzzle there is information that is critical to the task at hand, and then there’s other information that isn’t needed in real-time. Things like whether a device needs to be serviced in a weeks’ time, whether stock is going to be depleted by the end of the month, etc. Now consider a sensor having a fixed normal range, and only recording exceptions rather than all data all the time - you’re able to reduce the amount of data passed by around 60%- 90% in real time monitoring environments, as a basic statistic.

We are throwing away the rule book.

While the rest of the industry scrambles to figure out how to showcase the exponential value of IoT whilst also attempting to lock clients in to their technology stack, we’re taking the IoT rule book and throwing it out of the window.

We don’t care what technology our clients have now, and what technology they will have in five years’ time. We don’t talk about vendors, we talk protocols. We’re driving our clients to get to Big Data quicker, using what they have, thanks to our trademarked Raptor.

Raptor technology is the missing link in most of the discussions around digitalisation. A normalised, edge layer of physical and virtual intelligence that can be retrofitted, deployed and connected seamlessly into an ecosystem of existing technologies and things, radically reducing the cost and time of having to develop multiple edge integrations into disparate cloud applications.

The IoT.nxt Power-play Being able to retrofit onto all deployed devices, whether analog-, or IP-based has a huge benefit.

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Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ

It reduces disruption to business processes, cost of implementation, cost of training, cost and impact of enterprise-wide change management, reduces vulnerability and cyber risk because of less technology disparity at the edge, reduces data moving across the network that reduces the cost of the network and network congestion, reduces processing required at the cloud platform level as the data has already been curated at the edge, reduces the cost associated with maintenance of edge integrated gateways, has less attack surface at the edge as the gateways are rationalised and simplifies real-time subsystem integration

All of this allows us to better leverage the power of our cloud platform as we can now understand the up-, and downstream effects of an event-triggered occurrence and effect dynamic and seamless recalibration and interoperability throughout ALL edge connected devices.

We ensure that all the pieces of the puzzle are in the box, and ready to be pieced together to create the big picture.

Conclusion Edge normalisation of data at the edge gateway layer form the foundation for rapid digitalisation and digital transformation. The disruption that everyone talks about is vested in the ability for an organisation to continue its business but iteratively and rapidly start to address the core issues within its business through digitalisation. This leads to more visibility on a real-time basis allowing for dynamic recalibration back into the business ecosystem to achieve optimised levels of production and efficiency that bring about change and new ways of doing the same thing, better.

Peer-to-peer intelligence and learning will further drive this thinking – Raptor thinking - making us even more relevant as the necessity to drive edge analytics and decision making in critical business environments nullifies the cloud. Are you with me?

If you control the edge you unlock the cloud, a bottom up approach.

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Disruptive Technology and Changing Trends Influencing Business

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ew decades ago, reaching the moon was beyond imagination. But not today, because technology has completely changed the world and made it possible.

Technological inventions were revolutionized in the 18th and 19th century with the steam engine, the telegraph, fiber optics, typewriter, sewing machine, etc. Later, it changed the way we communicate on a real-time basis with telephone, radio, and internet.

Innovations in nanotechnology, biotechnology, and information technology are already helping to solve challenges that occur in these sectors. Through the breakthrough innovations in health services, technology has been able to improve the lives of poor people in developing countries.

Manufacturing field is increasingly being automated and technology driven. Advanced technology and systems such as automation, nanotechnology, cloud computing, the Internet of Things, and others are changing the face of manufacturing to improve business technologies. So, the adaption of technologies in work will revolutionize the way it was in the past in the field of manufacturing as well.

Let us see how trends in technology are changing businesses.

Internet of Things IOT has begun to change the world around us. It allows the businesses to access their information virtually, creating a flexible and global way of accessing data, any place, and any time. It reduces the cost by maintaining IT system, rather than purchasing expensive systems and equipment. It also allows employees to be more flexible in work practices. Let us see some fields where IOT must be adapted.

Healthcare Hospitals and other healthcare facilities are largely paperbased industry. The pen and paper approach is still followed largely around the world. Patient’s record sharing is still done in the traditional way which is time-consuming.

Whereas, real-time monitoring via connected devices can save lives in an event of a medical emergency. IOT devices collect and transfer health data and are stores in the cloud.

These data can be shared with a physician or a health firm, in order to allow them to look at it, regardless of their place, time or device. Therefore, in an event of an emergency, patients can contact a doctor who is many kilometers away with a simple smartphone.

Fleet Management Fleet operators spend a large amount of time, money and resource in maintaining the safety standards and resource in maintaining the safety standards and operate at the desired performance levels. Through various sensors, fleet companies have access to a vast amount of data. This information can help the company to make real-time quick decisions for instant improvements. In fact, these insights can help in effectively managing the overall supply chain.

Undoubtedly, IOT has set to become the backbone of the fleet management industry.

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Tech Infrastructure Public Transport Management In today’s major cities at rush hour, getting to and from work is a nightmare. Imagine a world where not only the cars are smart, but also the street and traffic lights. Public transportation systems like trains and buses are connected to individual’s smartphones.

This will help to know the exact time to leave the houses accordingly. In smart cities, passengers are already enjoying Wi-Fi and USB charging stations on public transportation. Overall, IOT already started affecting the aspects of our life.

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Renewable Energy Will you like earning money on reducing the use of electricity? Thanks to IOT energysaving tools, you can significantly decrease the numbers in your bills. IOT energy solutions are sensor-based technology. It analyses weather and environment condition, helps automate the management of wind farms, optimizes maintenance and thus reduces the cost dramatically. People (both households and companies) get a better understanding of their usage habits and adjusts them accordingly. These system collects data on electricity consumption in real-time and helps generate important insights for environmentalists, researchers, and conservation strategists. Thus, installing IOT smart energy device can join the environmental initiative, cut down on energy consumption and lessen the greenhouse effect.

Agriculture The global population is set to touch 9.6 billion by 2050. So, to feed this much population, the farming industry must embrace IOT. Smart farming based on IOT technology will reduce waste and enhance productivity. Ranging from the quantity of fertilizer utilized to the number of journeys the farm vehicles have made. In IOT based smart farming a system is built for monitoring the crop field with the help of sensors and automating the irrigation systems. It is highly efficient when compared with the conventional approach.

Thus, with the population growing rapidly, the demand can be successfully met, if the farmers implement agricultural IOT solutions in a prosperous manner. www.insightssuccess.com

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Blockchain Technology Blockchain technology is changing the way we do our day to day businesses. Companies are starting to work with Blockchain technology because it gives you privacy along with it is transparent. Let’s see how blockchain can help to deal with business.

Smart Contracts Contract is where consent of the parties is involved to agree and interact with each other. Blockchain technology helps to guarantee the validity of a transaction through a secure validation mechanism. Industries and institutions are heavily reliant on contracts, such as insurance, financial institutions, real estate, construction, entertainment and, law. A smart contract helps formalize the relationships between people, institutions and the assets they own. They eliminate the need for trusted third parties and are self-verifying, self-executing and Tamper resistant.

Blockchain will be an important part of our financial and technological digital future. It is one of the incredibly creative inventions that technology has ever seen. So how we use it is up to us, it could indeed transform the global scenario.

Technology and changing trends in businesses is not something which is going to happen in the future, it is happening right now. It has already started affecting a lot of businesses. So businesses have tremendous opportunity to benefit from such technological advancement. There is no doubt that technological innovations are largely followed all over the world and it will revolutionize the businesses.

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