TPOT AutoML-Guided Optimization of a Heterogeneous Stacking Ensemble for P2P Lending Default Prediction
DOI:
https://doi.org/10.12928/biste.v8i5.17316Keywords:
P2P Lending, TPOT AutoML, Heterogeneous Stacking, Credit Risk Prediction, ROC-AUCAbstract
Default risk in peer-to-peer (P2P) lending can cause substantial losses for investors and platforms, requiring classification models with reliable discrimination and probability estimates. However, conventional credit-risk pipelines often use manually configured models and provide limited exploration of complementary algorithm families. The research contribution is a reproducible TPOT AutoML-guided heterogeneous stacking framework that integrates family-constrained pipeline search, leakage-free nested evaluation, calibration, significance testing, and ablation analysis. TPOT was selected because its exportable scikit-learn-compatible pipelines and customizable operator spaces permit explicit control of learner families and feature selection. The experiments used 27 pre-loan attributes from 200,000 unique Lending Club records covering 2007–Q3 2020, divided into 20,000 discovery records and 180,000 locked evaluation records. TPOT optimized Random Forest, CatBoost, and LightGBM pipelines, whose out-of-fold probabilities were combined with a prespecified Hist Gradient Boosting learner using a CatBoost meta-learner and sigmoid calibration. Final performance was estimated using stratified nested cross-validation with 10 outer and three inner folds, while the classification threshold was fixed from the discovery partition. The proposed model achieved a ROC-AUC of 0.7123 (95% CI: 0.7094–0.7152), accuracy of 0.6694, recall of 0.6266, F1-score of 0.4249, and Brier score of 0.1425. Its ROC-AUC gains over Hist Gradient Boosting, LightGBM, Random Forest, and Logistic Regression remained significant after Holm correction, whereas differences from CatBoost and XGBoost were not significant. The framework therefore provides competitive discrimination and reliable calibration, although its gains over the strongest boosting baselines were marginal and external validation beyond a single Lending Club sample remains necessary.
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Copyright (c) 2026 Much Aziz Muslim, Dwika Ananda Agustina Pertiwi, Jumanto Jumanto, Ari Nugroho Putro, Sidiq Noor Kharisma, Pramudya Kamal Jumblat, Shahrul Nizam Salahudin, Yosza Dasril
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