Intelligent credit scoring using deep learning methods

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%.

Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach

“…In the case of the "Statlog (German Credit Data)" [22], the current study achieved an accuracy of 78.30%. While this accuracy is an improvement over some earlier studies, it falls short of the highest accuracy reported for this dataset, which was 88.89% by Gicić et al [14]. For "South German Credit " [23], this study achieved an accuracy of 77.80%.…”

“…The outcome of the experiments showed that the presented method achieved significant results in Australian credit score data and some progress on the German loan approval data. In Gicić et al [14], stacked unidirectional and bidirectional LSTM networks were applied to solve credit scoring tasks. The proposed model exploited the full potential of the three-layer stacked LSTM and BiLSTM architecture with the treatment and modeling of public datasets.…”

AI in credit scoring: A comprehensive review of models and predictive analytics

“…This architecture enables the automatic extraction of hierarchical features from data. In credit scoring, deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), enhance the understanding of complex relationships within diverse datasets (Gicić et al, 2023). Deep learning excels when confronted with large and unstructured data, providing a robust framework for credit risk assessment.…”

Ensemble Learning with Feature Optimization for Credit Risk Assessment

“…The development of rule-based and evolutionary models, such as the one proposed by Soui, Gasmi, Smiti, and Ghédira (2019), and the application of deep learning by Gicić, Ðonko, and Subasi (2023), have signiĄcantly improved the interpretability and accuracy of credit risk assessments. Mahajan et al (2022) contributed to the Ąeld by employing a Gaussian process-based approach for uncertainty handling in credit risk predictions.…”