Historically, the consumer loan industry has restricted itself to using relatively simple machine learning models and techniques to accept or deny loan applicants, because they are easy to understand, easy to explain, and familiar to regulators. However, more powerful (but also more complicated) methods can significantly improve business outcomes.
Sean Kamkar outlines a framework for evaluating, explaining, and managing these more complex methods. Along the way, Sean explores the need within and barriers surrounding the credit underwriting space and offers definitions for concepts around explainability.
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