{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:22:18Z","timestamp":1783131738778,"version":"3.54.6"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Loan default prediction is a critical task for financial institutions, directly influencing risk management, loan approval decisions, and profitability. This study evaluates the effectiveness of machine learning models, specifically XGBoost, Gradient Boosting, Random Forest, and LightGBM, in predicting loan defaults. The research investigates the following question: How effective are machine learning models in predicting loan defaults compared to traditional approaches? A structured machine learning pipeline is developed, including data preprocessing, feature engineering, class imbalance handling (SMOTE and class weighting), model training, hyperparameter tuning, and evaluation. Models are assessed using accuracy, F1-score, ROC AUC, precision\u2013recall curves, and confusion matrices. The results show that Gradient Boosting achieves the highest overall classification performance (accuracy = 0.8887, F1-score = 0.8084, recall = 0.8021), making it the most effective model for identifying defaulters. XGBoost exhibits superior discriminatory power with the highest ROC AUC (0.9714). A cost-sensitive threshold-tuning procedure is embedded to align predictions with regulatory loss weights to support audit requirements.<\/jats:p>","DOI":"10.3390\/systems13070581","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T09:45:52Z","timestamp":1752572752000},"page":"581","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Data-Driven Loan Default Prediction: A Machine Learning Approach for Enhancing Business Process Management"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8920-3604","authenticated-orcid":false,"given":"Xinyu","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8040-9281","authenticated-orcid":false,"given":"Tianhui","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, Northeastern University, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingmin","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianchen","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Florida International University, Miami, FL 33199, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Material Engineering, Florida International University, Miami, FL 33199, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanhao","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Politics and International Relations, Florida International University, Miami, FL 33199, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2416-0641","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Arts & Sciences, University of Miami, Miami, FL 33124, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"ref_1","unstructured":"Kisutsa, G. 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