E.T.-RNN: Applying Deep Learning to Credit Loan Applications

TL;DR

This paper introduces a deep learning approach using RNNs for credit scoring that outperforms traditional methods and yields significant financial benefits for a European bank.

Contribution

The paper presents a novel RNN-based method for credit scoring using detailed transactional data, demonstrating superior performance and practical advantages over existing approaches.

Findings

RNN-based credit scoring outperforms baseline models

Significant financial gains observed in pilot study

Method offers practical advantages for banking industry

Abstract

In this paper we present a novel approach to credit scoring of retail customers in the banking industry based on deep learning methods. We used RNNs on fine grained transnational data to compute credit scores for the loan applicants. We demonstrate that our approach significantly outperforms the baselines based on the customer data of a large European bank. We also conducted a pilot study on loan applicants of the bank, and the study produced significant financial gains for the organization. In addition, our method has several other advantages described in the paper that are very significant for the bank.