Iran Finance Association Iranian Journal of Finance 2676-6337 10 1 2026 01 01 Predicting Corporate Loan Defaults Using Deep Learning Algorithms and a Comparative Analysis with Linear Models: A Case Study of a Major Commercial Bank 1 42 244431 10.30699/ijf.2025.444059.1460 EN Mohammad Ahmadi Azar PhD. Candidate, Department of Financial Management, Faculty of Financial Engineering, Kish campus, University of Tehran, Kish, Iran. Reza Tehrani Prof., Department of Financial and Insurance Management, Faculty of Management and Accounting, College of Management, University of Tehran, Tehran, Iran Seyed Mojtabi Mirlohi Assistant Prof., Department of Finance, Faculty of Management, Shahrood University, Shahrood, Iran. 0000-0002-7332-0000 Journal Article 2024 02 18 <span>In today's complex economic landscape, accurately predicting events such as customer loan defaults presents a significant challenge for financial institutions. Traditional methods have shown limitations in accuracy, prompting the adoption of data-driven machine learning techniques for enhanced predictive capabilities. This study investigates the efficacy of novel machine-learning algorithms compared with linear models for predicting loan defaults at a major commercial bank. Data from over six thousand customer loan files spanning 2019 to 2022 were collected, cleaned, and clustered based on key loan indicators. The accuracy of predicting loan defaults was first evaluated using popular machine learning classification models, including LightGBM, XGBoost, Multilayer Perceptron, and Logistic Regression, and XGBoost performed best. After that, prediction accuracy was evaluated using various time-series machine learning algorithms, with a particular focus on a combined Gradient Boosting and Long Short-Term Memory (LSTM) approach. Results indicate that the combined algorithm outperforms traditional linear models, showing a substantial 40% improvement over the ARIMA algorithm in predicting loan default behavior. This study underscores the potential of advanced machine learning techniques to enhance predictive accuracy in the banking sector, offering valuable insights for risk assessment and financial decision-making.</span> https://www.ijfifsa.ir/article_244431_ec04a67155fc7d175b3b9a6764f92778.pdf