What do a million observations have to say about loan defaults? Opening the black box of relationships

Published

Journal Article

© 2017 Elsevier Inc. Using a unique dataset of more than 1 million loans made by 296 German banks, we evaluate the impact of many aspects of customer–bank relationships on loan default rates. Our research suggests a practical solution to reducing loan defaults for new customers: Have the customer open a simple transactions account – savings or checking account. Observe for some time and then decide whether to make a loan. Loans made under this model have lower default, as banks can use historical data about their borrowers to establish a baseline against which new client-related information can be evaluated. Banks assemble this historical information through relationships of different forms. We define relationships in many different ways to capture non-credit relationships, transaction accounts, as well as the depth and intensity of relationships, and find each of these can provide information that helps reduce default – even establishing a simple savings or checking account and observing the activity prior to loan granting can help reduce loan defaults. Our results show that banks with relationship-specific information act differently compared with banks that do not have this information both in screening and subsequent monitoring borrowers which helps reduce loan defaults.

Full Text

Duke Authors

Cited Authors

  • Puri, M; Rocholl, J; Steffen, S

Published Date

  • July 1, 2017

Published In

Volume / Issue

  • 31 /

Start / End Page

  • 1 - 15

Electronic International Standard Serial Number (EISSN)

  • 1096-0473

International Standard Serial Number (ISSN)

  • 1042-9573

Digital Object Identifier (DOI)

  • 10.1016/j.jfi.2017.02.001

Citation Source

  • Scopus