Artificial intelligence and relationship lending

Summary

Focus

The Great Financial Crisis and technological advancements have significantly transformed the banking system. Following the crisis, the banking sector faced compressed interest rate margins, negatively affecting profitability. In response, many banks further reduced traditional bricks-and-mortar branches and invested in data gathering and processing, taking advantage of artificial intelligence (AI) technology. With its enhanced analysis of hard, verifiable and codifiable data, AI can coexist with more traditional methods of reducing asymmetric information between banks and firms, such as acquiring soft information through close relationships between intermediaries and clients.

Contribution

We study the interaction between banks' adoption of AI in credit scoring and relationship lending. Using a unique data set on Italian banks' investments in AI for integrating their credit scoring techniques, matched with credit register data from one year before and one year after the outbreak of the Covid-19 crisis, we investigate whether relationship-based lending and new technology-driven financial intermediation complement or substitute each other, both in normal times and during the Covid crisis.

Findings

We find that, for a given duration of the lending relationship with a firm, using AI techniques for screening and monitoring mitigates the rent extraction of relationship lending in normal times. However, during the Covid crisis using AI did not provide additional credit or interest rate protection. Thus, AI helps banks to reduce the typical countercyclical effects of relationship lending on firms' credit supply, as well as on their investment and employment decisions.

Abstract

We study the interaction between banks' adoption of artificial intelligence (AI) in credit scoring and relationship lending. Using a unique dataset on Italian banks' investments in AI for the purpose of integrating their credit scoring techniques, matched with credit register data from one year before and one year after the outbreak of the Covid-19 crisis, we find that AI investments help banks mitigate the typical countercyclical effects of relationship lending on firms' credit supply, as well as on their investment and employment decisions.

JEL classification: G01, G21, E50

Keywords: artificial intelligence, machine learning, credit supply, relationship lending