AI in Financial Services – What are the Rules?
Introduction Introduction Iftach Drori Head of Marketing
Let’s keep it short, Iftach has over 10 years of Sales and Marketing experience. He holds a B.A in Economics and Management from the Tel Aviv Academic College. He joined Shield in 2018 and since then has managed to say the term “provide value” 545,892 times.
Financial Financial services services and and market market fraud fraud Financial services and market fraud; two phrases that you’d never expect to hear uttered in the context of a technology wild, wild West. Yet that’s the picture painted behind the recent RFI (Request for Information) posted by five of the world’s largest financial regulators. These include the Federal Reserve, Consumer Financial Protection Bureau, Federal Deposit Insurance Corp., National Credit Union Administration, and Office of the Comptroller of the Currency.
is there any unconscious bias that has inadvertently been built into the models? This potentially exposes a murkier side of financial transactions, one that we haven’t seen in nearly 20 years since Enron’s headquarters was raided by the FBI. “Do they, or don’t they?” has once again become the burning question. This time, however, it’s being posed to the whole financial industry as a collective. And it’s not only the financial regulators asking the question.
Implicit Bias in Machine Learning Let’s step back a moment for a quick primer into how implicit bias has apparently crept into financial risk assessment models. First, machine learning (ML) is an applied form of AI. The term originated to describe the practice of how the machine (in this case, the mathematical algorithm versus a physical machine entity like a robot) learns to associate X with Y after it has been exposed thousands or millions of times to data that explicitly “states” X = Y.
Think about it this way, when you do a Google images search, you’re actually on the receiving end of an application of AI and ML. The software engineers behind that technology chose which data sets would be initially presented to the search algorithm. This first step is known as “training the algorithm.” As is the case for training of any sort, you learn whatever you’re trained to learn. Once the algorithm has been trained, it is then tested on randomized data.
A Call for Governance
The wild, wild West analogy here refers to a lack of self-agency and policing which leads to an “anything goes” type of environment. Even in an industry as tightly regulated as financial services, there is no existing set of standards or rules governing how AI and ML (and the underlying training data) can be used in the assessment of financial risk.
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