360factors CEO Carl McCauley was invited for an interview on the Risk Management Show podcast, hosted by Boris Agranovich. The 24-minute discussion touches upon banking and fintech partnership trends and compliance readiness. Presented is an excerpt from the interview. Listen to the complete interview here.

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There has been a lot of talk surrounding the benefits that AI and Machine Learning may offer to regulatory compliance and risk management. Carl McCauley was asked about his perspective on how AI automates risk and compliance needs, enabling institutions to move beyond issue detection with predictive and proactive insights.

Here is what Carl McCauley had to say:

Timely Risk Data

I’ll give you some examples of what is happening in some banks. I talked to a risk officer about a year and a half ago. He described his scenario where he would go into a risk committee meeting on a quarterly basis, and he would compile risk reports and metrics right before that meeting. Come to find out that the data he’s presenting is maybe two or three months old, and in some cases, maybe four or five months old.

When you look at Covid, things change within a matter of months if not weeks, so many banks are asking how do we get quicker and more real-time in bringing that data in? That has been a trend that we have seen over the last year.

Identifying Risk Trends with Internal and External Data

Capturing KRIs and using that to detect problem areas – that is where people are at today. We are looking at how AI can help improve that. How do you not just look at that data, how do you see trends? You may look at a number of complaints and KRI data may still be with your threshold, but if you combine that with the trend of the complaints, you can detect that this is going up. That may be a problem area. You can then go out and combine that with regulatory insights, such as enforcement actions. Where are the enforcement actions and the fines coming from? What is in a particular regulation? Use that data to compare what you organization is doing. That can help predict Is this an area that we may want to explore? While we think we have a low risk, this is a big issue for the industry, and we need to make sure we’re involved with that.

Data Analysis & AI

There is a ton of data, such as call performance reports for banks. We report that, but how do you combine that data with interest rates, trends, construction starts, and then look at that data over the past 20 years? There is a lot of data out there, so how do you process that? AI is great at data modeling and analytics, and by using it you really can understand if we’re at 2% rate or 3% interest rate, what happens when it goes to 4%? What does that mean to us, and what do we need to prepare for? And that can allow a more of a proactive approach whereby during the risk committee meeting you’re now discussing What do we need to do to prepare for the next six months? That allows the bank to not only make sure they are prepared for, but that they can take advantage of the opportunities that might be present.. That is where we see that AI can really help: data modeling, data analytics, and combining that with not only internal KRIs but with all the data from the external side.

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