E.T.-RNN: Applying Deep Learning to Credit Loan Applications
This addresses credit risk assessment for banks, but appears incremental as it applies existing deep learning methods to a specific domain.
The paper tackled credit scoring for retail banking by applying RNNs to fine-grained transactional data, demonstrating significant outperformance over baselines and financial gains in a pilot study.
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.