Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Peer-to-Peer Money Lending with Dynamic Interest Rate Optimization Using Random Forest Regression Model

Authors
S. Niveditha1, *, R. Adithya1, K. M. Janagar1, Kousalya Senthilkumar1
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamilnadu, India
*Corresponding author. Email: nivedits@srmist.edu.in
Corresponding Author
S. Niveditha
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_76How to use a DOI?
Keywords
Peer-to-Peer Lending; Loan Classification; Interest Rate Prediction; Machine Learning; Random Forest; Regression Models; Risk Assessment; Financial Technology
Abstract

This study aims to enhance the performance and equity of Peer-to-Peer (P2P) lending systems by applying machine learning techniques for borrower assessment and interest rate forecasting. The proposed framework operates in two phases. Initially, a Random Forest Classifier is used to assess borrowers’ financial data and determine their loan eligibility. Subsequently, various regression techniques—such as Poisson Regression, Gamma Regression, Linear Regression, Random Forest Regressor, XGBoost Regressor, and Ridge Regression—are employed to estimate personalized interest rates for each borrower. This tailored approach seeks to offer fair, risk-sensitive interest rates, thus improving transparency, minimizing loan default risks, and optimizing borrower-lender matching. The two-phase machine learning model facilitates real-time decision-making and dynamic interest rate adjustment within P2P lending platforms.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_76How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - S. Niveditha
AU  - R. Adithya
AU  - K. M. Janagar
AU  - Kousalya Senthilkumar
PY  - 2025
DA  - 2025/10/31
TI  - Peer-to-Peer Money Lending with Dynamic Interest Rate Optimization Using Random Forest Regression Model
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 942
EP  - 954
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_76
DO  - 10.2991/978-94-6463-866-0_76
ID  - Niveditha2025
ER  -