Predicting Loan Amount Using Regression Models: A Machine Learning Approach

DOI

10.2991/978-94-6463-858-5_84How to use a DOI?

Keywords

Loan Prediction; Machine Learning; XGBoost; Financial Risk Assessment; Credit Scoring

Abstract

Loan approval forecasting is a key application of machine learning in the financial sector, supporting banks and financial institutions assessing the creditworthiness of users. This study presents details such as a machine-based approach to predict loan amounts based on applicants, income, credit history, employment status, and demographic factors. Data records provide a wide range of pre-processing, including lack of value added, functional scaling, and categorical coding, to ensure data consistency and model efficiency. Ridge regression models are implemented within a pipeline framework that uses numerical and categorical characteristic transformations for accurate prediction. The system is provided with the help of a flask web application that allows users to enter details and receive a loan amount forecast in real time. The integration of data reprocessing, model training and web-based provisioning provides a comprehensive scalable solution for predicting automated loan amounts. The results demonstrate the efficacy of inverse regression in multicollinearity treatment and guaranteed robustness treatment. This research contributes to the field of financial technology by providing accessible and efficient forecasting tools for credit valuation.

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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TY  - CONF
AU  - S. Archana
AU  - K. Ajay Kumar
AU  - G. Anish Reddy
AU  - Raghavendra Gowda
PY  - 2025
DA  - 2025/11/04
TI  - Predicting Loan Amount Using Regression Models: A Machine Learning Approach
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1007
EP  - 1017
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_84
DO  - 10.2991/978-94-6463-858-5_84
ID  - Archana2025
ER  -