APPLICATION OF DEEP LEARNING FOR CREDIT RISK PREDICTION IN THE BANKING SECTOR

Abstract

This article synthesizes modern deep learning approaches to estimating default risk for borrowers and credit portfolios, with an emphasis on deploying probability of default (PD) models under real-world regulatory and business constraints. We review neural architectures that are commonly applied to tabular and sequential financial data, including multilayer perceptrons (MLP) for structured scoring features, recurrent networks (RNN/LSTM/GRU) and temporal convolutional networks (TCN/CNN) for payment and behavioral histories, and transformer-based models for multivariate time series as well as credit graphs. Model quality is discussed through the lens of both ranking and calibration: ROC-AUC, PR-AUC, KS statistic, Brier score, calibration slope, expected calibration error, and recall/precision in risk-based top segments, compared against linear baselines and tree-based ensembles.

A separate focus is placed on data quality and practical pitfalls that strongly influence empirical results: class imbalance, label sparsity, and temporal shifts. We outline robust countermeasures such as class weights, focal loss, synthetic minority over-sampling (SMOTE) when appropriate, and threshold optimization driven by an explicit loss function and risk appetite rather than “accuracy” alone. The paper emphasizes time-series aware validation, leakage prevention, and out-of-time backtesting to ensure stability under drift.

Given that credit decisioning requires transparency, we examine interpretability and compliance practices that enable the use of complex models in regulated environments. Global and local explanations with SHAP and LIME are considered alongside monotonic constraints, feature stabilization, and model documentation within model risk management (MRM). We also describe how deep learning models can be integrated into production credit pipelines and MLOps: monitoring of covariate and label drift, alerting rules, scheduled retraining, fairness testing, and reproducible evaluation. Finally, we discuss combined approaches (stacking/ensembles) and graph neural networks for capturing relational dependencies and detecting fraud or risk propagation in borrower networks. Practical recommendations are provided for selecting architectures, tuning hyperparameters, calibrating PD outputs, and translating metric gains into economic effect via cut-off policy, expected loss, and portfolio-level profitability, while acknowledging limitations such as deployment costs, compute requirements, overfitting risk, and ethical considerations.