What Data Do I Need to Fight Application Fraud?

It’s raining data left and right, and you need to start by understanding what data you already have to fight application fraud and where there are gaps.

by Liz Lasher

January 17, 2018

In previous blogs, we’ve defined application fraud, explored the role of hackers and cyber terrorists in perpetrating fraud, and the reflected on the importance of establishing a fraud risk appetite. A key takeaway from all of these posts is that to strengthen defenses across the enterprise, we must improve synergies between data, technology and analytics. When it comes to application fraud, our framework must seamlessly bridge intelligence across the customer lifecycle to enable smarter decisioning.

That brings me to the topic that comes up in every application fraud conversation: data. While the value of data may seem self-evident — particularly with the global investments in digital transformation strategies leading to faster, more faceless decisions — one question inevitably surfaces: What kind of data do I actually need?

Data from Inside and Outside

It’s raining data left and right, and reaping the benefits of this data downpour requires understanding what information you already have at your fingertips and where there are gaps.

Financial institutions often underplay the usefulness of their own data assets, or, in some cases are unaware of powerful data assets that reside within other functions of their own organization, including Compliance, Risk and even Marketing.

Vendors also play a critical role in the data landscape, providing innovative solutions that are derived from cross-industry experiences (not just your institution) and with roadmaps that evolve over time to meet changing market needs. With the digitization boom, the number of relevant data assets continues to rapidly expand, with examples include device profiling, email reputation, biometrics – whether behavioral or physical – and more.

These technologies are subject to breach as well, however. For example, in China, we’ve seen fraudsters already exploit “selfie with your driver’s license” controls that leverage facial recognition (comparing the selfie to the driver’s license photo) and optical character recognition (reading the driver’s license information to compare to the supplied application data). To that end, a new level of physical biometrics is being explored that includes movement analysis in video sources. We anticipate that leveraging this type of personal data will spark an interesting privacy and regulatory debate in the years to come.

Your Data Checklist

What are the key data elements you should be considering in your application fraud detection ecosystem? The following list is designed to help you inventory:

One important factor to bear in mind is that no matter how smart this data is, the utility will be limited if any data source is used in isolation. Rather than evaluating data via point-in-process solutions — e.g., the applicant’s physical biometrics passed checks, now how about their email reputation — an integrated decision across all of your data elements provides the optimal ROI to create an informed decision.

Consult Your Data Strategy

Before you run out to buy data missing from your list, consider this: You have to balance the value the data adds with the investment in integration efforts adding this data will require. We recommend that prioritizing your data gaps to align with your overall data strategy is critical.

A realistic and robust data strategy requires understanding risk at the channel, product and regional level to help assess not only the viability of data, but also prioritize where there are anticipated or existing vulnerabilities.

The following graphic provides several questions that can guide you in defining your data strategy.

With application fraud on a meteoric rise, financial institutions must take full advantage of the vast amounts of the data available to them. Agility is key to ensure fraud controls are faster and smarter than the bad guys. Implementing a unified data hub that brings together key information from all data sources is critical. Accessed via a single holistic point, centralized data assets can galvanize powerful machine learning analytics.

In fact, machine learning is a topic for one of my upcoming blogs. So stay tuned.

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This is largely because they have trade lines that have been open longer, which leads to higher scores (assuming the trade lines are in good standing and all other things equal). The most credit savvy among you will remember that length of credit history accounts for roughly 15% of the overall <a href="http://www.myfico.com/CreditEducation/WhatsInYourScore.aspx" target="_blank">FICO® Score calculation</a>. But for younger consumers, it isn’t too helpful to say, “just wait until you are 50 years old, and that will fix things!” Thus, I decided to dive deeper with our analysis, including looking at the credit behavioural characteristics of younger consumers with high FICO® Scores. In this analysis, I looked at credit characteristics for the two highest score bands, 750-799 and 800-850, which have about 16% and 23% of the population, respectively. I further broke this down by looking at the characteristics of the youngest 15% in each group, which occurs at age 29 for the 750-799 group and 43 for the 800-850. We used FICO® Score 9 for our analysis, and examined typical variables of the categories that go into the make-up of the score. <a href="http://www.fico.com/en/blogs/wp-content/uploads/2016/10/FICO-Score-High-Achiever-table.png"><img class="alignnone size-full wp-image-29367" src="http://www.fico.com/en/blogs/wp-content/uploads/2016/10/FICO-Score-High-Achiever-table.png" alt="fico-score-high-achiever-table" width="715" height="747" /></a> Since there’s a lot to dissect in this chart, I’ll highlight a few findings that struck me: <ul> <li>Credit history. Higher scores are undoubtedly associated with a longer time on file. What’s striking is the higher average time on file for the 800+ group, both overall and for revolving accounts (the most common trade line). Clearly, it helps to start credit use early in life – but that’s not the sole determining factor for high scoring. The younger cohorts in both the 800+ and 750-799 groups have time on file medians significantly less than their older counterparts.</li> <li>New credit. High credit scorers do, in fact, pursue new credit. We see little variation in months since last trade line opening and last inquiry among all groups observed.</li> <li>Pay on time. As you might expect, the further in the past the delinquency, the higher the score – and that’s if a delinquency ever happened. 95% of the 800+ people and 72% of the 750-799 group have never been delinquent, which is a striking contrast to the 57% for the total population. The percentages for the younger groups are even higher, especially so for the 750-799ers. Collection items are also scarce for these high scorers. While 23% of the national population have collection items greater than $99, the figures are only 0.5% for the 800+ group and 3% for the 750-799 group. It is clear that, in conjunction with starting a credit file early, paying on time is a key trait of the FICO® Score high achiever.</li> <li>Balance and utilization. Finally, we look at how credit is being used, focusing on revolving product use. (Since most people only have one mortgage or one auto loan, variation in instalment product figures is minimal and driven by payment history, which is near perfect for high scorers, as we’ve seen.) FICO® Score high achievers tend to have more open revolving accounts. While the % active trades and utilization are generally lower, by no means do high scorers avoid the use of credit products. What drives the difference here is the amount of available revolving credit utilized. The 800+ group is roughly 1/4 of the national median, both in total and on their highest utilized card; they also have lower balances and more cards. The 750-799 group is more active and higher utilized, but still only at 2/3 of the national figures. The young 750-799 group compensate with lower revolving balances and, as a result, lower utilization, which is in line with what we see for the 800+ group.</li> <li>Benchmarks. I’d like to point out that while this study provides good benchmarks of the typical credit traits of the FICO® Score high achiever, individual credit profiles will vary widely. And keep in mind that when we sort groups of consumers as a whole by different behaviours, some results may look superficially odd. For example, the 800+ band has a longer median average open account time (128 months) than average revolving months on file (140 months).</li> </ul> So, what do we conclude? A huge takeaway is that high scorers don’t have too much in the way of serious delinquencies and negative public record data. In most respects, the young 800+ people are like their peer group. They really only differ markedly in their shorter credit history and higher non-mortgage balance, both of which are a function of life stage. Our results show that you don’t have to be a credit product hermit; these high scorers have more credit cards. The takeaway is to start credit activity early and maintain well-managed and moderate usage. In many ways, the young 750-799 people are more interesting because their age profile is much younger. Their credit characteristics differ more from their peer group, particularly in terms of length of credit history. They compensate by having almost pristine credit records and lower balances. Ultimately, 39% of US consumers are FICO® Score high achievers, which they accomplish by following common-sense credit practices. It ain’t rocket science!

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