ItemConference Paper

Managing Retail Credit Default Risk Using Artificial Intelligence and Machine Learning: Ethical Considerations and Challenges

Title
Managing Retail Credit Default Risk Using Artificial Intelligence and Machine Learning: Ethical Considerations and Challenges
Creator
Siby, Amala; Jha, Babita
Description
Credit risk management is an integral aspect of financial stability of financial institutions. The determinants of credit default behavior help in identifying types of credit risk based upon customers habits or actions such as purchasing behavior, spending patterns, social interactions, and moral hazard. The awareness on such behavioral factors helps in getting a complete picture in analyzing the credit worthiness of any individual. The onset of artificial intelligence (AI) and machine learning (ML) has remodeled the methodologies of credit risk assessment. Training and developing learning models to predict the borrowers behavior evolved along with AI and ML, developing advanced mechanisms for managing credit default risks. Using risk modeling to determine behavioral scoring enables the banks to acquire creditworthy retail borrowers, alongside its inherent demerits of the models. This study is focused on the employment of AI and ML techniques along with qualitative determinants in managing credit risk, drawing attention to the advantages and disadvantages and applications and implications for the financial service providers. The paper examines contemporary literature on modern methods of credit risk management and sheds light on the technological accuracy of credit risk calculations and automated decision-making processes alongside methods to alleviate biases. The study also outlines the sequential phases of the conventional models along with the regulatory and ethical considerations of AI-ML-driven credit risk assessment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Source
Lecture Notes in Networks and Systems;Volume;1509 LNNS;pp.351-359
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Artificial intelligence; Credit risk assessment models; Data-driven decision models; Ethical considerations in credit default models; Machine learning; Retail credit
Coverage
Siby A., School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India; Jha B., School of Business and Management, Christ University, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981968792-3;
Format
online
Language
English
Type
Conference paper

Citation

Siby, Amala; Jha, Babita, “Managing Retail Credit Default Risk Using Artificial Intelligence and Machine Learning: Ethical Considerations and Challenges,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/25615.

Output Formats