AI-Driven Risk Assessment in Blockchain-Based Asset Tokenization

Abstract

The banking industry's credit risk management is changing due to the quick uptake of blockchain technology and artificial intelligence (AI), which improves operational effectiveness, regulatory compliance, and prediction accuracy. While blockchain offers safe, transparent, and unchangeable transaction records, AI uses algorithmic learning, natural language processing, and statistical analysis, to evaluate counterparty risk, predict liquidity requirements, and identify early warning signs. This study compares conventional techniques with cutting-edge data-driven approaches to investigate the combined effects of artificial intelligence and digital currencies on credit risk assessment. Data was gathered from secondary sources and financial experts using a comprehensive analytical and descriptive methodology, and both machine learning and statistical methods were used for analysis. Results show that AI-powered counterparties valuation, policy as software surveillance, robotic corporate actions, and immediate updates greatly enhance investor experience, tokenized asset lifecycle management, and credit risk assessment. Accuracy, reliability, and operational resilience are guaranteed by alignment with legal structures like the NIST AI Risk handling Framework. The study comes to the conclusion that combining blockchain with AI offers a strong basis for safe, effective, and legal financial systems, particularly as symbolization and digital assets expand internationally.