Credit Scoring Based on Behavioural Data

Abstract: Credit modelling has traditionally been done by credit institutes based on financial data about the individuals requesting the credit. While this has been sufficient in lowering risk in developed economies with plenty of financial data it is inefficient in developing economies and fails to reach the unbanked population. As this is both limiting many responsible consumers from getting access to credit as well as limiting companies from reaching paying customers, it is evident that new strategies for credit modelling are needed. This paper explores the usage of behavioural data for credit modelling gathered from users of Klarna’s app. The models are based on the machine learning algorithms logistic regression, random forests, neural networks, and gradient boosted decision trees. In this study, models were trained on Swedish data in multiple timespans and tested in different timespans and countries. The results show that modelling on the data points developed in this study is effective and suggest that in certain cases be used in predicting new and unknown markets by training on similar markets.