Intelligent Analysis of Users Data for Investment in Digital Financial Platforms Using Machine Learning Algorithms

Authors

https://doi.org/10.48314/jidcm.v2i2.86

Abstract

Today, due to the high risk of investing in emerging markets, the application of Machine Learning (ML) to design systems tailored to specific financial environments has rapidly expanded, providing new opportunities for analyzing financial data and improving decision-making. Peer-to-Peer (P2P) lending, a form of crowdfunding, has attracted considerable attention from corporate investors, institutional investors, and credit rating agencies by providing an alternative source of financing for individuals, businesses, and entrepreneurs. In this study, to address the class imbalance problem in P2P lending credit scoring, undersampling, oversampling, and synthetic minority oversampling techniques were employed in conjunction with six widely used ML classifiers including Multilayer Perceptron (MLP), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), and Light Gradient Boosting Machine (LGBM) based on a large dataset from the Lending Club website. The performance of the models was evaluated using four metrics:  precision, accuracy, recall, and the F1 score. The experimental results show that artificial minority oversampling with MLP method has the best performance due to its higher value in the three criteria compared to other methods, as well as previous studies, and achieved an Area Under the Curve (AUC) value of 0.925 in the Receiver Operating Characteristic (ROC) curve, indicating high class discrimination. The findings of this study provide a comprehensive, data-driven framework that can guide credit risk assessment and support decision-making in real-world financial risk management.

Keywords:

Peer-to-peer lending, Online platform, Financial risk, Machine learning, Classification, Imbalanced data

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Published

2026-06-08

Issue

Vol. 2 No. 2 (2026): Journal of Intelligent Decision and Computational Modelling

Section

Articles

How to Cite

Torkian, V. ., Bamdad, S. ., & Sarfaraz, A. H. . (2026). Intelligent Analysis of Users Data for Investment in Digital Financial Platforms Using Machine Learning Algorithms. Journal of Intelligent Decision and Computational Modelling, 2(2), 101-111. https://doi.org/10.48314/jidcm.v2i2.86

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