Combating Fraud and Money Laundering: How the Financial Services Industry Can Leverage Big Data

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1 A Poit of View Combatig Fraud ad Moey Lauderig: How the Fiacial Services Idustry Ca Leverage Big Data Abstract Fiacial istitutios across the globe are losig a cosiderable amout of their aual icome to fraud. The rapid evolutio of techology, chagig customer buyig patters, ad the emergece of e-commerce have led to a icrease i fraudulet trasactios. Also, the risig threats of olie fraud, terrorism fiacig, drug traffickig, ad moey lauderig have resulted i a icreased focus o regulatory compliace. While striget regulatios are beig eforced to combat both fraud ad moey lauderig, baks ad fiacial istitutios eed to make sigificat ivestmet to esure compliace. I additio, they eed to maitai sizeable provisios for o-compliace pealties. Big Data ad aalytics ca play a key role i helpig fiacial istitutios prevet fraud ad moey lauderig. The greatest challege that fiacial istitutios face i implemetig traditioal risk prevetio ad aalytics programs is esurig that routie busiess operatios or customer experieces are ot disrupted. Fiacial istitutios are leaig towards a data aalysis solutio that ca help them predict attacks based o idustry treds. This is where Big Data comes i. It has the ability to support cotiuous or behavioral autheticatio, which ca prevet fraud ad idetify the source of moey i suspected lauderig activities without disruptig routie operatios. This paper discusses the role of Big Data i prevetig fraud-related losses ad idetifyig moey lauderig activities i the fiacial services idustry. Fraud Prevetio Challeges Facig Fiacial Istitutios With icreasig competitio, it is imperative for fiacial istitutios to seek ways to improve their reveues, ad at the same time, cotrol costs. I order to do this, they eed to better uderstad customer eeds, provide trasparet processes, stregthe their risk maagemet practices, ad combat fiacial crime. Aided by sophisticated techology, the scope of fiacial crime has exteded to retail bakig, olie brokerage, private bakig, cards, isurace, corporate bakig, ad istitutioal securities, amog others. The rapid growth i the umber ad complexity of electroic paymet trasactios across the globe has also ushered i ew fraud-related challeges. As paymet systems evolve ad real time settlemets become more prevalet, paymets-related fraud throws up a buch of challeges. Fraudsters have stepped up their use of electroic etwork attacks ad iovated to develop ew patters of fraud across electroic paymet chaels.

2 Baks ad fiacial istitutios are therefore ivestig heavily i fraud detectio ad prevetio. Some of these ivestmets are ecessitated by the ature of busiess ad some by regulatios. Fraud ad moey lauderig related challeges are ot limited to fiacial losses. They ca result i loss of reputatio as well. The challeges istitutios face i combatig fraud iclude: Need for combatig fraud effectively without hiderig customer service Idetificatio of the source of moey ad the beeficiary of trasactios Limited viability of traditioal trasactio moitorig systems with high icidece of false positives ad persoel costs Heavy pealties imposed due to o-compliace with AML policies Limited or iaccurate customer iformatio gathered i the 'Kow Your Customer' (KYC) process A eterprise wide approach to compliace ad risk maagemet is required to combat the more sophisticated crimials. Most compaies today are cosolidatig their fraud fightig ad AML efforts ito a sigle departmet to help adopt techology eterprise-wide, ad maage global compliace efforts cetrally. To effectively address fiacial crime, fiacial istitutios eed to adopt a proactive rather tha a reactive approach to fraud maagemet. Big Data ad aalytics ca play a key role i detectig ad prevetig fraud, give the large data sets that baks ad fiacial istitutios have access to. They ca be used to detect crime hotspots ad predict where crimes are likely to occur i the ear future. Adoptig a Proactive Approach to Fraud Maagemet with a Shared Operatios Model Most baks employ real time trasactio moitorig with rule-based egies that help them pick out risky trasactios based o static defiitios i their system. These trasactios are the reviewed to determie whether a particular trasactio is fraudulet or geuie. However, fraud is dyamic. The rules also have to be dyamic to esure that chagig fraud patters ca be quickly idetified. As ad whe a fraudulet tred is idetified, fiacial istitutios iclude it i the rules egie to esure that similar trasactios are preveted i the future. Fraud idetificatio rules are geerally set based o: The fraudulet treds idetified by the fiacial istitutio Trasactio patters of the istitutio's customers Iformatio o treds shared by the bakig ad fiacial idustry associatios Today, there is a log lead time betwee the start of a tred ad its idetificatio ad mitigatio by fiacial istitutios. A tred idetified by oe istitutio is ot immediately available for other istitutios to couteract or prevet losses, ad fraudsters take advatage of this lag period. To devise ad deploy effective fraud maagemet operatios, global fiacial istitutios eed to employ fraud maagemet experts who uderstad the relevat tools ad techology that idetify treds. They 2

3 also eed to move quickly to address the idetified treds. The faster they are able to modify their rules ad strategies i lie with a tred, the more moey they ca save. A shared operatios model wherei fiacial istitutios work with a experieced fraud mitigatio services parter ca help mitigate the risks i the traditioal sceario. Uder this model, the parter has the requisite data, treds, ad aalyses that ca be used to effectively formulate strategies ad combat fraud at multiple istitutios simultaeously. The Role of Big Data ad Aalytics i Fightig Fraud Big Data ca provide iformatio such as the frequecy with which users access their accouts, the device from which the accout is accessed, user demographics, the userame ad password combiatio, ad the speed with which the user types the userame ad password. Aalysis of this data ca help i settig up parameters ad algorithms to uderstad customer behavior better. Big Data ad Behavioral Aalytics To detect actual istaces of fraud ad elimiate false positives, fiacial istitutios ca build a holistic view of their customers with data from a variety of sources, icludig mobile phoes ad social media. This iformatio ca also be used to distiguish fraudulet activity from ormal activity. For example, if a card holdig customer forgets to alert the fiacial istitutio about his travel plas, a fraud detectio system aided by Big Data ca gai isight from the customer's mobile ad social media data. It ca idicate the locatio that the customer is travelig to ad correlate the recet charges icurred o his card to reduce false positives. Isider fraud is detrimetal to ay orgaizatio, ad eve more so i the case of fiacial istitutios. The risk is greatly magified if a employee colludes with orgaized fiacial crime groups, resultig i fiacial ad reputatioal losses to the orgaizatio. This ca be idetified ad elimiated with the help of Big Data aalytics, which ca dyamically study employee behavior ad trigger alerts o abormal activity or situatios. Big Data ad Moey Lauderig Moey lauderig has become a serious problem due to several sigificat chages i world markets. Some of these chages iclude the itesificatio of iteratioal trade, emergece of a global fiacial system, removal of barriers to iteratioal travel, ad a surge i orgaized feloy. These factors have combied to provide the meas for covertig illegal proceeds ito seemigly legitimate fuds. Moey lauderig ca impact the soudess of baks ad fiacial istitutios, ad ca eve destabilize autoomous atios. Due to digital advacemets, crimials are able to quickly reassig large sums of moey across coutries. Eve five such trasfers ivolvig sigificat amouts of moey ca distort the demad for moey at a macroecoomic level, ad produce uhealthy volatility i iteratioal capital flows ad exchage rates. For ay risk compliace uit, the primary cocer i wire trasfers is to uearth the hidde relatioships betwee accouts by performig a lik aalysis as early as possible. With the availability of huge 3

4 quatities of raw data, the lik aalysis ca be doe much more quickly ad accurately by leveragig Big Data. Thus, trackig the source ad the beeficiary of the fuds has become much easier tha before. Big Data also plays a key role i idetifyig relatioships betwee accouts, joit accout holders, couterparties, as well as politically exposed persos (PEP) ad their family or associates. Traditioal trasactio moitorig systems ca also be optimized to achieve a lower false positive ratio. Detectig Fraud i Real Time Machie learig ivolves the cofiguratio of alert triggers or trasactio moitorig systems that ca iteract ad share iformatio with each other. It idetifies the patters i data ad simultaeously lears from it to deliver efficiet isights, helpig fiacial istitutios detect fraud i real time as well as idetify the future potetial for fraud. The efficiecy of rules ca be ehaced by reducig the rate of false positives geerated by the existig trasactio moitorig systems; this reduces the cost of maual review ad ehaces the quality of fraud detectio. As metioed earlier, traditioally, fiacial istitutios have relied o red flags or alerts to detect fraud ad moey lauderig. However, i recet times, they have bee deployig advaced aalysis ad predictive models to geerate alerts oly i the case of suspicious trasactios. The ability to test the scearios with real-time productio data is a added advatage. It helps aalysts test ad maitai rules or thresholds to reduce false positives, as well as the umber of alerts for review. Usig Big Data to Pre-empt ad Combat Fraud Fiacial crime cotiuously adapts itself to elude detectio. As ew schemes ad moey lauderig ad fraud techiques become kow, the eed for a solutio that ca create ew rules or establish methods to discover ew patters is critical. The holistic reach of eterprise-wide solutios ca help orgaizatios bled iformatio accumulated from differet uits ad scrutiize disparate data to create a ew class of busiess ad customer itelligece. Ivestig i Big Data ad the relevat expertise ca thus sigificatly ehace the success rate of fraud detectio activities. Early adopters of Big Data are likely to sigificatly beefit from its ability to effectively detect ad mitigate fraud ad moey lauderig activities. The ideal solutio for fiacial istitutios today is to utilize Big Data aalytics i a cost-effective model that allows early detectio ad prevetio of fraud. Orgaizatios ca achieve this by workig with parters who offer platform solutios ad services leveragig Big Data aalysis i a shared operatios model. About the Author Bashyam Selvaraj Bashyam Selvaraj is part of the Eterprise Fraud ad Ati-moey Lauderig (AML) operatios team i the Busiess Process Services (BPS) domai at Tata Cosultacy Services (TCS). He has participated i ad led process ehacemet ad trasitio projects for fraud detectio ad AML. Bashyam has over 10 years' experiece i the bakig ad fiacial services domai, with core expertise i fraud detectio, Kow Your Customer (KYC), ad AML. He holds a Bachelor's degree i Computer Sciece ad Master's degrees i Iformatio Techology ad Busiess Admiistratio. 4

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