Articlegold2025

Optimizing Credit Risk Prediction for Peer-to-Peer Lending Using Machine Learning

Lyne Imene Souadda · Ahmed Rami Halitim · Billel Benilles · José Manuel Oliveira · Patrícia Ramos

13 citations0/0 files storedForecasting

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Financial Distress and Bankruptcy Prediction FinTech, Crowdfunding, Digital Finance Machine Learning and Data Classification

Abstract

Hyperparameter optimization (HPO) is critical for enhancing the predictive performance of machine learning models in credit risk assessment for peer-to-peer (P2P) lending. This study evaluates four HPO methods, Grid Search, Random Search, Hyperopt, and Optuna, across four models, Logistic Regression, Random Forest, XGBoost, and LightGBM, using three real-world datasets (Lending Club, Australia, Taiwan). We assess predictive accuracy (AUC, Sensitivity, Specificity, G-Mean), computational efficiency, robustness, and interpretability. LightGBM achieves the highest AUC (e.g., 70.77% on Lending Club, 93.25% on Australia, 77.85% on Taiwan), with XGBoost performing comparably. Bayesian methods (Hyperopt, Optuna) match or approach Grid Search’s accuracy while reducing runtime by up to 75.7-fold (e.g., 3.19 vs. 241.47 min for LightGBM on Lending Club). A sensitivity analysis confirms robust hyperparameter configurations, with AUC variations typically below 0.4% under ±10% perturbations. A feature importance analysis, using gain and SHAP metrics, identifies debt-to-income ratio and employment title as key default predictors, with stable rankings (Spearman correlation > 0.95, p<0.01) across tuning methods, enhancing model interpretability. Operational impact depends on data quality, scalable infrastructure, fairness audits for features like employment title, and stakeholder collaboration to ensure compliance with regulations like the EU AI Act and U.S. Equal Credit Opportunity Act. These findings advocate Bayesian HPO and ensemble models in P2P lending, offering scalable, transparent, and fair solutions for default prediction, with future research suggested to explore advanced resampling, cost-sensitive metrics, and feature interactions.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 13 citations exist upstream.

References

2012Practical Bayesian Optimization of Machine Learning Algorithms 2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 2017A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring 2014Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending 2018Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning 2015Risk assessment in social lending via random forests 2015Determinants of Default in P2P Lending 2020A benchmark of machine learning approaches for credit score prediction

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.