Multidisciplinary Journal of Science and Technology

THE IMPACT OF ARTIFICIAL INTELLIGENCE-BASED RISK ASSESSMENT SYSTEMS ON THE QUALITY AND SPEED OF FINANCIAL DECISION-MAKING

Allamurodova Fotima

Termez State University, Faculty of Economics, student of the Finance and Financial Technologies

Keywords: Artificial intelligence, risk assessment, financial decision-making, machine learning, credit scoring

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Abstract

This article examines how artificial intelligence-based risk assessment systems enhance both
the quality and speed of financial decision-making processes. As financial institutions grapple with
increasingly dynamic markets and complex data environments, traditional risk assessment tools
struggle to process real-time information effectively. AI systems—leveraging machine learning,
natural language processing, and large language models—promise rapid, accurate risk evaluation,
superior predictive insights, and operational scalability. Drawing on empirical analyses and global
case studies, this study delves into the effectiveness of AI-driven methodologies, comparing them to
conventional models in domains such as credit scoring, market volatility prediction, and fraud
detection. It further considers associated risks including algorithmic bias, data privacy, model
transparency, and systemic vulnerability due to widespread use of similar AI systems
ft.com+7flagright.com+7ijrpr.com+7. Results highlight that while AI systems significantly improve
decision speed and accuracy, they require robust data infrastructure, comprehensive governance
frameworks, and explainability mechanisms to ensure ethical and resilient deployment. This articleconcludes with policy recommendations aimed at optimizing financial AI systems, balancing
innovation with oversight

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