English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We use machine learning techniques to investigate whether it is possible toreplicate the behavior of bank managers who assess the risk of commercial loansmade by a large commercial US bank. Even though a typical bank already relieson an algorithmic scorecard process to evaluate risk, bank managers are givensignificant latitude in adjusting the risk score in order to account for otherholistic factors based on their intuition and experience. We show that it ispossible to find machine learning algorithms that can replicate the behavior ofthe bank managers. The input to the algorithms consists of a combination ofstandard financials and soft information available to bank managers as part ofthe typical loan review process. We also document the presence of significantheterogeneity in the adjustment process that can be traced to differencesacross managers and industries. Our results highlight the effectiveness ofmachine learning based analytic approaches to banking and the potentialchallenges to high-skill jobs in the financial sector.

Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?