Machine Learning-Based Risk Assessment and Default Prediction in P2P Lending Platforms

Bhupesh Rawat; Julia Nathalie; Danny Manongga; Gabriel Fransiso; Fitra Putri Oganda Moana; Po Abas Sunarya

Authors

Bhupesh Rawat Graphic Era Hill University Bhimtal Author https://orcid.org/0000-0001-7445-760X
Julia Nathalie ilearning incorporation Author
Danny Manongga Satya Wacana Christian University Author https://orcid.org/0000-0002-7430-8740
Gabriel Fransiso Ilearning Incorporation Author
Fitra Putri Oganda Moana Eduaward Incorporation Author
Po Abas Sunarya Association of Indonesian Private Higher Education Institutions Author https://orcid.org/0000-0002-3869-2837

Keywords:

Machine Learning, Peer to Peer, Prediction, Information System, Data Driven

Abstract

This study investigates machine learning-based risk assessment and default prediction in peer-to-peer (P2P) lending platforms, addressing increasing concerns related to credit risk, information asymmetry, and platform sustainability in digital financial ecosystems. The primary objective is to evaluate the effectiveness of machine learning models in predicting default risk and to identify key borrower, loan, and platform-level determinants influencing default outcomes. To achieve this objective, a mixed-method approach is employed, integrating quantitative analysis of publicly available loan-level data with qualitative case studies of selected P2P platforms. The quantitative component utilizes advanced machine learning algorithms, including ensemble learning methods, to model and predict default probability, while the qualitative analysis explores platform practices in risk evaluation, pricing strategies, and default management mechanisms. The findings demonstrate that machine learning models significantly outperform traditional credit scoring methods in predicting default risk, particularly when incorporating alternative data sources and behavioral features. Key determinants of default include borrower income stability, debt-to-income ratio, loan tenure, interest rate, and platform-specific risk policies. Furthermore, qualitative insights reveal that transparent risk communication and proactive monitoring mechanisms are critical in reducing default rates and enhancing platform resilience. In conclusion, the study highlights that the integration of machine learning techniques into risk assessment frameworks enhances predictive accuracy and supports sustainable P2P lending operations. It also underscores the importance of model transparency, explainability, and regulatory alignment to strengthen trust among stakeholders in digital lending environments.

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