Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?
This addresses the potential for automation in high-skill financial jobs, though it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of replicating bank managers' intuitive adjustments to algorithmic credit risk scores using machine learning, finding that algorithms could effectively mimic human behavior with significant heterogeneity across managers and industries.
We use machine learning techniques to investigate whether it is possible to replicate the behavior of bank managers who assess the risk of commercial loans made by a large commercial US bank. Even though a typical bank already relies on an algorithmic scorecard process to evaluate risk, bank managers are given significant latitude in adjusting the risk score in order to account for other holistic factors based on their intuition and experience. We show that it is possible to find machine learning algorithms that can replicate the behavior of the bank managers. The input to the algorithms consists of a combination of standard financials and soft information available to bank managers as part of the typical loan review process. We also document the presence of significant heterogeneity in the adjustment process that can be traced to differences across managers and industries. Our results highlight the effectiveness of machine learning based analytic approaches to banking and the potential challenges to high-skill jobs in the financial sector.