Defending against fraud is difficult even in the best of times, and these are not the best of times.  The gains achieved by making card transactions more secure have pushed fraud to the main gate of entry: the application. 

In the most notorious form of application fraud, organized crime rings open accounts using stolen identities or synthetic identities they create.  Then, they establish transaction histories and make payments to undermine pattern detection technology and to support repeated line increases, often for months or years before they finally “bust out” with run-ups in charges and subsequent defaults. 

While bust-out fraud rings can bring enormous losses within a single case, a more pervasive and under-reported application fraud threat involves consumers who misrepresent their ability to repay debt by inflating their credit qualifications, such as income. They rationalize their actions by telling themselves that a “little white lie” to get around the terms and conditions of credit is not a criminal offense. Financial institutions tend to perpetuate this first-party fraud by failing to recognize the default that results as fraud. Instead, it is most often reflected as a charge-off on the bad debt ledger. 

Bolstering the integrity of the application process with more robust screening technology and advanced analytics is a critical way to gain ground in the battle against fraud.  However, many institutions have been slow to act. Instead, they put customers through unfriendly security checks and increase their manual review rates. The result is lost business, customer dissatisfaction, and unnecessary strain on fraud personnel.   

Advanced fraud analytics avoids these problems by instantly assessing risk and resolving minor inconsistencies for straight-through processing of good applicants. Truly suspect applications are routed for further automated checks or manual reviews.  Additional analytics come into play to assist reviewers with faster and more effective secondary analysis and verification. This combination of improved application screening and productivity tools for reviewers reduces the risk of application fraud, offers a better customer experience and improves the overall efficiency and scalability of the fraud prevention unit.

Still, as fraud moves to the most vulnerable points of entry, there remains a magnified threat to institutions across the board.  Strengthening security with more advanced screening is a relatively easy step.  What is often harder to get is up-to-the-moment data on the fraudulent applications underway across multiple institutions for use in real-time fraud prevention efforts.  Criminal rings know this and carefully plan their strikes to occur across institutional, geographic and industry boundaries.

To combat this practice, financial institutions may participate in reciprocal data sharing arrangements which provide access to a pool of private data for fraud screening purposes. Some have held out or sought to limit participation to only a few institutions, citing commercial and competitive sensitivities.   However, this stance ultimately backfires by creating blind spots wherever data coverage, timeliness or depth is lacking. 

Fraud data sharing enables institutions to receive more timely and complete data on known frauds and they receive earlier warning of suspicious patterns.   Unfortunately, data protection and privacy regulations vary by country and sometimes work against data sharing.  This tends to push fraud to regions with the most restrictive data sharing policies.  Nonetheless, data sharing is a powerful fraud prevention strategy worth pursuing to the extent possible.  

But perhaps the most important strategy in the fight against fraud is to fundamentally challenge the notion that a certain amount of misrepresentation and the associated bad debt losses are, “a cost of doing business.” The truth is that many institutions are making significant gains by better protecting themselves from fraud committed by their own customers.  The most obvious benefits are better quality applications, lower charge-offs, and improved margins.   Taking all of these together, institutions can make a strong case for freeing up much needed capital reserves. 

A good starting point is to perform a retrospective review of customer accounts.  By applying advanced analytic techniques to these accounts, firms can clearly quantify the extent of delinquencies and bad debt resulting from undetected frauds and identify the unique characteristics predictive of fraud.  These predictors can then be incorporated into application screening in a continuous, closed loop process for a more effective and sustainable approach to fraud prevention.   

While the problem of fraud is growing in complexity, the business issue remains straightforward: less effective fraud prevention strategies result in higher losses from fraud and bad debt, higher overhead costs and lower profits.  Advanced fraud analytics, data sharing and reviews of bad debt files to uncover hidden fraud are key strategies firms can use to identify and combat key threats.   

What all of these strategies have in common is necessity.  The relentless assault from criminals around the globe, consumers seeking to circumvent credit risk policies, and business pressure to do more with less will not be going away any time soon.  Institutions that make it a priority to strengthen their defences against application fraud early on will avoid becoming a weak link adversely selected by criminals for a disproportionate share of fraud. On a larger scale, they will be joining best practice firms worldwide in using the latest technology and advanced analytics to work smarter in the battle to stay ahead of fraud.