A Global Hub of Payments Data for Fighting Fraud

The Falcon Intelligence Network is designed to drive fraud-focused AI innovation while providing a global perspective of fraud detection performance.

by TJ Horan

Vice President, Product Management

August 6, 2018

For more than 25 years, the Falcon consortium has served as the world’s largest collection of payment card transaction details, consisting of the diverse fraud and non-fraud truncation assets that have spawned 80+ fraud-focused artificial intelligence and machine learning patents, with 30+ patents pending. It has provided a “force multiplier” effect that benefits contributing members with a continual flow of innovation.

While the original charter of the consortium still holds true, the consortium itself has evolved since its inception. For example, the data updates that were once monthly have now become daily and the types of data elements being contributed have expanded to reflect digital channels and new payment interactions. This evolution has resulted in additional use cases that expand the types of models developed while opening new opportunities to deliver global fraud intelligence.

For this reason, the term “Falcon consortium” no longer seemed sufficient when describing the breadth of capabilities coming to market over the course of the next year. With this in mind, we are proud to recognize the rapidly expanding role of the FICO fraud consortium with a more appropriate name: the FICO^® Falcon^® Intelligence Network – the core of FICO’s enterprise fraud solutions.

What the Falcon Intelligence Network Is — and Isn’t

The Falcon Intelligence Network is very different from consortium-based business models associated with credit bureaus and data aggregators. In those models, the goal is to develop depth and breadth of historical data in order to maintain a record of fraudulent activity (e.g., a negative file). Similar databases exist for valid email addresses, device IDs, and identity details associated with fraud events.

The Falcon Intelligence Network is not a database of known “fraudsters” or “suspicious identities.” It is a collection of de-identified transactional and non-monetary event data, which is properly labeled to indicate fraud and non-fraud transactions in order to model suspicious and legitimate behaviors. In fact, the data is completely anonymized via a hashing algorithm. This approach allows FICO’s data scientists to research and develop new predictive features and machine learning algorithms without exposing account-specific details.

More than 9,000 global institutions contribute the transactional and non-monetary data needed for FICO to create machine learning predictive features aimed at differentiating non-fraud and fraud activity. These behavioral analytics reduce false positives while interpreting even the most subtle indications of fraud. The resulting models work with Falcon’s real-time profiling techniques to interpret individual behaviors while benefitting from macro-level fraud patterns derived from data contributed by thousands of financial institutions. These concepts apply across supervised fraud models as well as unsupervised and semi-supervised models that are trained on bank-specific data sets.

Beyond predictive feature R&D and machine learning model development, the data richness of the Falcon Intelligence Network opens the door to a wide array of new fraud detection services. A sampling of these services may include:

Card compromise detection – The ability to monitor cross-bank transactions in order to proactively detect indications of data loss and compromise while enabling banks to identify and properly treat cards with a high risk of exposure.
Model simulation – Enabling customers to improve model monitoring processes and evaluate ROI metrics by simulating the performance of new modeling techniques.
Performance benchmarking – The world’s only source of fraud-focused comparative benchmarking across peer group institutions.

Clearly, the Falcon Intelligence Network is more than a consortium of data. It has evolved to become a fresh, interactive hub of payment-specific Big Data. It is designed to drive fraud-focused AI innovation while providing a global perspective of fraud detection performance and limiting banks’ exposure to data breaches. The Falcon Intelligence Network is a unique asset in the evolution of digital payment fraud.

TJ Horan

TJ Horan is vice president of Product Management at FICO, leading the Solution Product Management team for Lifecycle Products. TJ began his career with DuPont, where he helped design and maintain Expert Systems for process control. Since then, TJ has held a variety of technical and management roles in the fields of analytics, fraud and risk management, and decision management at industry leading companies such as HNC Software and SAS. TJ holds a B.S. in computer science and a M.S. in statistics, both from the University of South Carolina.

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FICO’s latest research on <a href="http://www.fico.com/en/blogs/risk-compliance/us-credit-quality-rising-the-beat-goes-on/" target="_blank">national FICO® Score distribution</a> shows that US credit quality continues to trend upwards, with an increasing number of consumers scoring in the highest scores ranges. Given that, we decided it was time to refresh our study on “<a href="http://www.fico.com/en/blogs/risk-compliance/infographic-what-does-a-high-credit-scorer-look-like/" target="_blank">FICO® Score high achievers</a>,” where we examine the credit behavioural profiles of consumers with higher credit scores. As expected, our latest study showed that older people generally have higher scores, as has been the case in our <a href="http://www.fico.com/en/blogs/risk-compliance/age-before-beauty-credit-worthiness-before-youth/" target="_blank">past research</a>. 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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