AI - What Chief Compliance Officers Care About
Arguably, there are more financial institutions located in the New York metropolitan area than anywhere else on the planet, so it was only fitting for a conference on AI, Technolog…
Arguably, there are more financial institutions located in the New York metropolitan area than anywhere else on the planet, so it was only fitting for a conference on AI, Technology Innovation & Compliance to be held in NYC – at the storied Princeton Club, no less. A few weeks ago I had the pleasure of speaking at this one-day conference, and found the attendees’ receptivity to artificial intelligence (AI), and creativity in applying it, to be inspiring and energizing. Here’s what I learned.
CCOs Want AI Choices
As you might expect, the Chief Compliance Officers (CCOs) attending the AI conference were extremely interested in applying artificial intelligence to their business, whether in the form of machine learning models, natural language processing or robotic process automation – or all three. These CCOs already had a good understanding of AI in the context of compliance, knowing that:
- Working the sets of rules will not find “unknown unknowns”
- They should take a risk-based approach in determining where and how to divert resources to AI-based methods in order to find the big breakthroughs.
All understood the importance of data, and how getting the data you need to provide to the AI system is job number one. Otherwise, it’s “garbage in, garbage out.” I also discussed how to provide governance around the single source of data, the importance of regular updating, and how to ensure permissible use and quality.
AI Should Explain Itself
Explainable AI (XAI) is a big topic of interest to me, and among the CCOs at the conference, there was an appreciation that AI needs to be explainable, particularly in the context of compliance with GDPR. The audience also recognized that their organizations need to layer in the right governance processes around model development, deployment, and monitoring––key steps in the journey toward XAI. I reviewed the current state of art of Explainable AI methods, and where their road leads to getting AI that is more grey-boxed.
Ethics and Safety Matter
In pretty much every AI conversation I have, ethics are the subject of lively discussion. The New York AI conference was no exception. The panel members and I talked about how any given AI system is not inherently ‘ethical’; it learns from the inputs it’s given. The modelers who build the AI system need to not pass sensitive data fields, and those same modelers need to examine if inadvertent biases are derived from the inputs in the training of the machine learning model.
Here, I was glad to be able to share some of the organizational learning FICO has accumulated over decades of work in developing analytic models for the FICO® Score, our fraud, anti-money laundering (AML) products and many others.
AI safety was another hot topic. I shared that although models will make mistakes and there needs to be a risk-based approach, machines are often better than human decision-making, such as autopilots on airplanes. Humans need to be there to step in if something is changing, to the degree that the AI system may not make an optimal decision. This could arise as a change in environment or data character.
In the end, an AI system will work with the data on which it has trained, and is trained to find patterns in it, but the model itself is not necessarily curious; the model is still constrained by the algorithm development, data posed in the problem, and the data it trains on.
Open Source Is Risky
Finally, the panel and I talked about AI software and development practices, including the risks of open source software and open source development platforms. I indicated that I am not a fan of open source, as it often leads to scientists using algorithms incorrectly, or relying on someone else’s implementation. Building an AI implementation from scratch, or from an open source development platform, gives data scientists more hands-on control over the quality of the algorithms, assumptions, and ultimately the AI model’s success in use.
I am honored to have been invited to participate in Compliance Week’s AI Innovation in Compliance conference. Catch me at my upcoming speaking events in the next month: The University of Edinburgh Credit Scoring and Credit Control XV Conference on August 30-September 1, and the Naval Air Systems Command Data Challenge Summit.
In between speaking gigs I’m leading FICO’s 100-strong analytics and AI development team, and commenting on Twitter @ScottZoldi. Follow me, thanks!
Popular Posts
Average U.S. FICO® Score at 716, Indicating Improvement in Consumer Credit Behaviors Despite Pandemic
The FICO Score is a broad-based, independent standard measure of credit risk
Read more
The GSE Data Is Public. Here's What It Shows About Credit Score Modernization.
Independent Milliman analysis of GSE historical data confirms what anyone can now verify directly: FICO® Score 10T is the most predictive score.
Read more
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>
Read moreTake the next step
Connect with FICO for answers to all your product and solution questions. Interested in becoming a business partner? Contact us to learn more. We look forward to hearing from you.