Evaluating the impact of AI and blockchain on credit risk mitigation: A predictive analytic approach using machine learning
Dr. Papa Rao Maddala¹, Dr. Naresh Ogirala2, Indla Novak3, Thadi Nagini4.
1Professor and HoD of Department of MBA, Sree Vahini Institute of Science and
Technology (Autonomous), Tiruvuru. AP-521235. E-Mail: paparao070128@gmail.com
2Naresh Ogirala, Associate Professor, Master of Business Administration, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, AP-521230.
3Indla Navak, 23MG1E0032, Student of Sree Vahini Institute of Science and Technology (Autonomous) Tiruvuru. AP-5212352
4Thadi Nagini, 23MG1E0007 Sree Vahini Institutes of Science and Technology (Autonomous), Tiruvuru. Andhra Pradesh (AP) -521235
Abstract:
By combining blockchain technology, machine learning, and artificial intelligence (AI), the banking sector has witnessed a revolution in credit risk reduction in recent years. With an emphasis on predictive analytics and decentralized frameworks, this paper explores the real-world applications of these technologies in the discovery, evaluation, and management of credit risk. The study demonstrates how machine learning models, blockchain's transparent and unchangeable ledger systems, and AI-powered algorithms have greatly increased the precision and effectiveness of credit risk assessments through thorough literature analysis and case studies. The report also examines how financial institutions implement these technologies to improve operational risk management, lower fraud, and create more accurate credit scoring systems. Notwithstanding their promise, there are still significant obstacles to overcome, including data privacy, regulatory compliance, and implementation costs. In order to effectively utilize the advantages of AI, blockchain, and machine learning in reducing credit risk, the article ends with ideas for overcoming these obstacles.
Keywords: Artificial Intelligence; Blockchain; Machine Learning; Credit Risk Mitigation; Predictive Analytics; Financial Technology; Credit Scoring; Risk Management; Decentralized Finance; Operational Risk
