Black Knight Explores Strategies for Managing AI and Machine-learning in Mortgage Industry
February 22, 2023
While artificial intelligence (AI) and machine learning (ML) is increasingly being used to solve problems in the mortgage industry, these tools also present new—and different—risk and challenges, including when to apply them and how to address compliance requirements, according to a new white paper by Black Knight.
According to the paper, this technology operates much differently than traditional software solutions and demands new management disciplines.
“Science fiction author Arthur C. Clarke once wrote that ‘Any sufficiently advanced technology is indistinguishable from magic.’ That certainly applies to many applications of AI/ML, particularly so-called ‘black box’ algorithms,” said Rich Gagliano, president of Black Knight Origination Technologies. “In our daily lives in the digital world, AI is making things happen behind the scenes, with decisions being made and events triggered—all as if by magic. But in an industry as tightly regulated as ours, we can’t trust important lending decisions to magic. That’s why we’re offering this white paper to everyone in our industry. Regulators have justifiable concerns, particularly in matters of fair lending, that consumers are not harmed by bias in the application of AI/ML in the lending process. That is what makes model explainability and transparency so critical.”
The paper addresses the many challenges and industry-specific concerns that must be considered when bringing AI/ML into the mortgage process. It also details when AI/ML is the right approach, and when it is not. For example, traditional business rule management software remains the appropriate technology for meeting most mortgage origination requirements since inputs must be directly traceable to precise expected outputs – otherwise known as a deterministic solution. AI/ML technology is appropriate for addressing business problems that do not yield to deterministic approaches (i.e., where inputs are varied or a range of possible outcomes exists), and therefore a probabilistic approach is required.
“Mortgage industry executives face a challenge,” Gagliano said. “Existing accounting rules, government regulations, business process standards and auditing methods designed to assess traditional software are not viable when it comes to AI/ML, and yet there are no well-established standards to fix that. This paper attempts to bridge this gap for our industry, with a deep exploration of the things lenders should consider as the technology—and its oversight—progresses.”
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