Intelligent credit scoring using deep learning methods. (13th February 2023)

• Record Type: Journal Article
• Authors: Gicić, Adaleta; Đonko, Dženana; Subasi, Abdulhamit
• Is Part Of: Concurrency and computation. Volume 35:Number 9(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-13
• Subjects: credit risk management -- credit scoring -- deep learning -- stacked BDLSTM -- stacked LSTM; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7637
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Credit scoring is one the most important parts of credit risk management in reducing the risk of client defaults and bankruptcies. Deep learning has received much attention in recent years, but it has not been implemented so intensively in credit scoring compared to other financial domains. In this article, stacked unidirectional and bidirectional LSTM (long short‐term memory) networks as a complex area of deep learning are applied in solving credit scoring problems for the first time. The proposed robust model exploits the full potential of the three‐layer stacked LSTM and BDLSTM (bidirectional LSTM) architecture with the treatment and modeling of public datasets in a novel way since credit scoring is not a time sequence problem. Attributes of each loan instance were transformed into a sequence of the matrix with a fixed sliding window approach with a one‐time step. Our proposed models outperform existing and much more complex deep learning solutions thus we succeeded in preserving simplicity. In this article, measures of different types are employed to carry out consistent conclusions. The results by applying three hidden layers on the German Credit dataset showed an accuracy of 87.19%, for Kaggle dataset accuracy reached 93.69%, and for Microcredit dataset accuracy of 97.80%.