Evaluating the impact of AI and blockchain on credit risk mitigation: A predictive analytic approach using machine learning

ZAHIDUR RAHMAN FARAZI *

Department of Information Systems and Operations Management, University of Texas at Arlington.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(01), 575–582.
Article DOI: 10.30574/ijsra.2024.13.1.1707
Publication history: 
Received on 04 August 2024; revised on 10 September 2024; accepted on 13 September 2024
 
Abstract: 
In recent years, the integration of artificial intelligence (AI), blockchain technology, and machine learning has transformed credit risk mitigation strategies in the financial industry. This paper explores the practical applications of these technologies in identifying, assessing, and managing credit risk, with a specific focus on predictive analytics and decentralized frameworks. Through a comprehensive literature review and case studies, the research demonstrates how AI-driven algorithms, blockchain's transparent and immutable ledger systems, and machine learning models have enhanced the precision and efficiency of credit risk evaluations. Additionally, the study investigates how these innovations are being adopted by financial institutions to create more accurate credit scoring systems, reduce fraud, and optimize operational risk management. While these technologies hold great promise, challenges such as data privacy, regulatory compliance, and implementation costs remain significant barriers. The paper concludes with recommendations for overcoming these challenges and maximizing the potential of AI, blockchain, and machine learning in credit risk mitigation.
 
Keywords: 
Artificial Intelligence; Blockchain; Machine Learning; Credit Risk Mitigation; Predictive Analytics; Financial Technology; Credit Scoring; Risk Management; Decentralized Finance; Operational Risk
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0