Responsible AI in Credit Risk: FICO Insights at Edinburgh Conference 2023
FICO will deliver the AI keynote at the 2023 Credit Scoring and Credit Control Conference, as well as two other presentations related to Responsible AI
The 18th annual Credit Scoring and Credit Control Conference kicks off tomorrow, a premier global forum for showcasing innovation in responsible and ethical artificial intelligence (AI), machine learning (ML) and related data science. Sponsored by the University of Edinburgh’s Business School, this venerable technology event brings together credit professionals from across Europe and around the world to learn, share ideas and pursue the spark of inspiration.
AI is arguably today’s hottest business and technology topic, and I am honored to be on the mainstage at this prestigious event, delivering the AI keynote on Thursday. My presentation, “Responsible AI in Credit Risk: Palatable, Interpretable, Ethical, and Auditable,” illustrates how a standard interpretable AI model drives transparency in the application of Responsible AI to credit scoring –– an essential capability in enabling AI model explainability, the ability to expose imputed bias, address and remove that bias, and then audit model performance for ongoing accountability.
FICO’s recent machine learning and AI breakthroughs will be additionally presented by three data scientists, two of whom will give talks on patent-pending techniques for which I am co-inventor.
Explanation Dropout: Practical Counterfactual Explanations for Machine Learning Models
Matt Kennel, Senior Principal Scientist
Matt will discuss how AI model explainability can be improved by effecting the dropout of many variables that have the same explanation, thereby improving the “signal to noise” ratio in interpreting model explanation. This innovative approach can be superior to traditional methods such as the Shapley value.
First-to-Saturate Principle for Consistent Explanations of Neural Networks
Krzysztof Nalborski, Lead AI Scientist
Krysztof will share FICO’s novel approach to identifying the unique modes of activation of latent features in an analytic model that contribute deterministic explanation of neural networks, by limiting latent feature activation to the first mode to “max out” (saturate) first. This technique helps data scientists to better understand the properties and typically dense connections in the hidden layer(s) of neural networks, a key predictive component that is inherently challenging.
Developing Broad-Based Credit Scores to Balance Many Objectives
Gerald Fahner, Senior Principal Scientist
Gerald’s talk will illustrate how FICO applies cutting-edge data science responsibly to address real-world business and technology objectives. He will discuss FICO’s latest AI approaches to balance multiple desired scoring objectives, both high-level (predictive power, robustness, interpretability, fairness) and finer-grained goals such as predictive differentiation across various different loan types.
If you’re in Edinburgh, please come to my Artificial Intelligence keynote and talks to learn about the latest techniques on the forefront of achieving Responsible AI in credit scoring transparency. You’ll see why the Edinburgh conference provides a chance to test, discuss, and shape our innovation responsibly — a data scientist’s dream and a welcome break from the challenges of our daily work.
How FICO Can Help You Build Responsible AI
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FICO® Score High Achievers: Is Age the Only Factor?
<p>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.</p> <p>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>.</p> <p>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.</p> <p>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<strong>®</strong> Score 9 for our analysis, and examined typical variables of the categories that go into the make-up of the score.</p> <p><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></p> <p>Since there’s a lot to dissect in this chart, I’ll highlight a few findings that struck me: <ul> <li><strong>Credit history.</strong> 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><strong>New credit.</strong> 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><strong>Pay on time.</strong> As you might expect, the further in the past the delinquency, the higher the score – and that’s <u>if</u> 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><strong>Balance and utilization.</strong> 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 <u>available</u> 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<em>. </em>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><strong>Benchmarks.</strong> 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.</p> <p>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.</p> <p>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.</p> <p>Ultimately, 39% of US consumers are FICO® Score high achievers, which they accomplish by following common-sense credit practices. It ain’t rocket science!</p>
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