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

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