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Research Paper | Information Security | Volume 13 Issue 7, July 2024 | Pages: 1643 - 1649 | United States
Risk Decisioning for Unsecured Lending Using Gradient Boosted Decision Trees: Calibration, Governance, and Operational Trade-Offs
Abstract: Unsecured lending requires credit decisions under asymmetric error costs and strict governance constraints. Gradient boosted decision trees (GBDTs) are increasingly used for default-risk estimation because they capture nonlinearities, interaction effects, and heterogeneous feature types better than classical scorecards in many settings. This paper presents a decisioning-focused research blueprint for using GBDTs in unsecured lending, covering data definition, leakage control, temporal validation, probability calibration, threshold design, and explanation practices suitable for regulated credit workflows. The approach combines a GBDT probability-of-default model with a separate calibration layer and a policy engine that maps calibrated risk to approve/refer/decline outcomes and, where applicable, risk-based pricing or limit assignment. Hypothetical but operationally plausible results suggest improved rank-ordering performance and better tail separation than a logistic baseline, while highlighting that raw boosted outputs can be miscalibrated and require post-hoc correction. The discussion emphasizes stability under portfolio drift, reject-inference bias, and the practical limits of explainability and fairness criteria in credit. The paper concludes with research directions on robust learning under selection bias, constrained boosting, and drift-aware calibration.
Keywords: credit risk, unsecured lending, gradient boosted decision trees, calibration, explainability
How to Cite?: Ajay Punia, "Risk Decisioning for Unsecured Lending Using Gradient Boosted Decision Trees: Calibration, Governance, and Operational Trade-Offs", Volume 13 Issue 7, July 2024, International Journal of Science and Research (IJSR), Pages: 1643-1649, https://www.ijsr.net/getabstract.php?paperid=SR24727100805, DOI: https://dx.dx.doi.org/10.21275/SR24727100805