Risk Management & Analytics: Financial Institutions Proceeding with Caution in Race to Use Artificial Intelligence for Modeling

Financial Institutions Proceeding with Caution in Race to Use Artificial Intelligence for Modeling

A few years ago, “big data” emerged as a buzzword across many industries, including financial services. The concept and push was to capture all-encompassing information. While that talk has wound down, we’re now hearing an uptick in discussions about artificial intelligence and machine learning, interpreted as using big data to improve decision making.

Financial institutions are poised to be at the forefront of using AI and machine learning because they’ve had to maintain and sustain data for so long. Chat bots have been the most prevalent application of AI to date at large institutions, but AI is also being leveraged to prevent fraud, analyze legal contracts and even develop challenger models during validation.

Large and midsize institutions have also applied AI to some other areas of their modeling, as they’ve sought to ramp up of the sophistication of their analyses, but they’ve done this with caution for a number of reasons, according to Jeff Prelle, Managing Director and Head of Risk Modeling at MountainView Financial Solutions, a Situs company.

Prelle states that AI is starting to be used more frequently for reviewing loan applications and deciding whether to provide credit, a trend that originated with marketplace lending platforms. There are a number of Fair Lending Act concerns, and he points out that if you’re feeding data into models and not putting the correct theoretical constructs behind it, you could easily violate fair-lending practices.

There are three types of machine learning: supervised, unsupervised and semi-supervised. Each one has a different application and different level of associated risk in its application. Factor in the regulations surrounding lending requirements at financial institutions, and you realize that a model using unsupervised learning is not always going to work well without introducing theoretical constraints in the process, especially in credit modeling, according to Prelle.

In elaborating on this point, he emphasizes that the more data you collect, the more precise your result should be, but if you train the model incorrectly, you can still get an undesirable result.

“Business theory should bound what you’re going to feed the AI, and we have seen some people implementing AI outside of theory, utilizing data snooping,” said Prelle. “Some say the AI will be the end of statistical theory, but that’s really not always the case, because it’s math at the end of the day.”

The obvious supplement of this precautionary note, according to Prelle, is that it’s easy to improperly implement these models and improperly implement the data constructs. “If the data is bad, your model is going to be bad, whether it’s machine learning or not,” he explained. “You can build a theoretically sound model, but if the data is bad, you’re definitely going to get a bad result.”

Another challenge with implementing AI for financial models is that the model risk management guidance of Statement SR 11-7 from the Federal Reserve does not explicitly integrate AI as a part of the process, though many of same the principles apply. Prelle said some individuals using AI don’t know how to interpret or test it yet. “There’s a real danger in not having a good understanding of how to test it, how to use it, how to validate it, and how to make sure you’re not making bad decisions because of the points I just mentioned.”

In summarizing the challenges, Prelle said we are starting to view modeling through a very new type of lens: “You take away some visibility when you use AI, but it is not impossible to implement it well and verify the results, so you must temper it with theory in all phases of the model life cycle.”

Goldman Warns U.S. Mortgage Bond Returns Will Weaken

Goldman Sachs this week cautioned investors with U.S. mortgage-backed securities (MBS) holdings face the risk of below-average returns in the coming year due to rising bond yields and their current low-yield premiums over comparable U.S. Treasuries.

After adjusting for risk, MBS on average have produced 0.35 percentage point more than Treasuries in annual total return since 1998, Goldman Sachs analyst Marty Young said in a research note.

“We expect MBS returns over the next year to be lower than average historical returns, as rising yields will exert a downward drag on bond prices and tight mortgage vs. Treasury spreads will provide lower than average carry potential,” Young wrote.

Read more: Reuters

Foreclosure Rate at 11-year Low as Home Equity, Employment Grow

Unemployment lows and increased home equity paved the way for the lowest mortgage delinquency rates seen in 11 years, according to CoreLogic.

The foreclosure inventory rate fell 0.2% to 0.6% year-over-year in March, which was the lowest reading for the month in 11 years. The foreclosure inventory rate has held steady at 0.6% since August.

The early-stage delinquency rate, which serves as an indicator of mortgage market health, held steady at 1.7% in March from a year ago. For mortgages that were 60-89 days past due, the share also remained unchanged at 0.6%.

Read more: National Mortgage News

The Sharing Economy Comes To Bank Compliance

The cost and complexity of regulatory compliance remains a complex issue for banks of all sizes. Addressing anti-money laundering (AML) and Know Your Customer (KYC) regulations are one specific topic area, which while burdensome even for the largest banks, for mid-sized and smaller banks the cost of technology needed for compliance (and the difficulty of finding staff with the skills required to use such technology effectively) can be both a major financial and management concern.

In Accenture’s 2018 Compliance Risk Study, 89% of respondents said that investment in compliance will continue to rise over the next two years, with more emphasis on technology than on adding headcount.

New technologies such as sophisticated analytics, automation, machine learning and artificial intelligence have demonstrated their effectiveness in helping banks manage emerging risks, especially in areas such as financial crime and money laundering. Banks without access to these technologies (or skills needed to apply the technologies) may be at a disadvantage to competitors who are better equipped to prevent fraud and financial crime.

Read more: Forbes

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