AI-Based Credit Scoring Models for Loan Risk Assessment

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Published: 2026-02-08
DOI: https://doi.org/10.15662/IJRPETM.2026.0901018
Keywords:
Artificial intelligence, credit scoring, loan risk analysis, machine learning, prediction, financial risk

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Rajesh Aakula
Senior BI Architect, Leading Information Technology Company, Herndon, Virginia, USA

Abstract

Automated credit scoring with the help of AI algorithms is becoming a new trend in evaluating the creditworthiness of borrowers in financial institutions. Compared to most current credit scoring frameworks, which are relatively restrictive and confined to limited financial data, AI models include other data sources, besides transaction history and social media activity, for a more comprehensive assessment of a borrower's creditworthiness. The integration of AI models has been proven to enhance the models' accuracy rates and decrease loan default risks to as low as twenty per cent. In addition, these systems help increase financial integration since credit is extended to people who may be considered non-credit worthy by conventional credit scoring. Automation of loan risk assessment has also served as a determinant of operational efficiency in terms of time and costs. Finally, credit scoring systems based on artificial intelligence are more effective and fair for assessing loan risk, which benefits both the lender and the borrower. They are all set to revolutionize lending as the financial sector rolls out artificial intelligence in even greater measures in the future.

Article Details

Issue

Vol. 9 No. 1 (2026): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

AI-Based Credit Scoring Models for Loan Risk Assessment. (2026). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(1), 137-143. https://doi.org/10.15662/IJRPETM.2026.0901018

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