Can System Logs Enhance the Performance of Credit Scoring? – Evidence from an Internet Bank in Korea

Sunghyun Kyeong; Daehee Kim; Jinho Shin

doi:10.20944/preprints202110.0006.v1

Preprint Case Report Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 30 September 2021 / Approved: 1 October 2021 / Online: 1 October 2021 (11:32:58 CEST)

This study is the first to examine whether the performance of credit rating, one of the most important data-based decision-making of banks, can be improved by using banking system log data that is extensively accumulated inside the bank for system operation. This study uses the log data recorded for the mobile app system of Kakaobank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from Kakaobank's vast log data, we develop a credit scoring model by utilizing variables with high information values. Consequently, the discrimination power of the new model compared to the credit bureau grades was significantly improved by 1.84% points based on the Kolmogorov–Smirnov statistics. Therefore, the results of this study imply that if a bank utilizes its log data that have already been extensively accumulated inside the bank, decision-making systems, including credit scoring, can be efficiently improved at a low cost.

Credit scoring; Credit risk model; Big data; Digital footprints

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Can System Logs Enhance the Performance of Credit Scoring? – Evidence from an Internet Bank in Korea

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