Machine Learning-Based Credit Scoring for Personalized and Inclusive Lending in Consumer-Centric Financial Systems

Title

Machine Learning-Based Credit Scoring for Personalized and Inclusive Lending in Consumer-Centric Financial Systems

Creator

Balaji, K.; Rao, P. Seshagiri

Description

Traditional approaches to credit-scoring are largely based on rule systems that can be excessively fixed and limited to the ability to reflect individual financial behavior. The article analyzes the effectiveness of machine-learning (ML)- based credit ranking with the hypothesis that they can improve predictive capability and fairness of consumer credit lending. The performance of these algorithms, including supervised methods of learning, e.g., logistic regression, random forests as well as the deep learning, is contrasted to the conventional credit models. Model transparency is provided by SHAP values and other methods explainable by AI. Findings show that practice based on the use of ML outperform traditional methods in risk assessment, especially, through the inclusion of supplementary forms of data in traditional databases based on transaction behavior, virtual footprints, and psychometric signals. Furthermore, ethical standards and moral confidence in ML informed credit decision-making will require regulation-proof and explanatory modelling. Through the research, it is recommended to implement policy measures intended to cause financial institutions, fintech companies, and regulating bodies to implement ML-based credit-scoring technologies, with fairness and predictive effectiveness being reciprocal drivers of financial access and consumer-friendly lending practices. 2025 IEEE.

Source

Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Credit Scoring; Explainable AI; Financial Inclusion; Machine Learning; Personalized Lending

Coverage

Balaji K., Christ University, Bengaluru, India; Rao P.S., The Apollo University, School of Technology, Andhra Pradesh, Chittoor, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833159676-7;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICCIST67338.2025.11438375

https://www.scopus.com/pages/publications/105037455739?origin=resultslist