  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.
