The Role of AI and Machine Learning in Financial Risk Assessment

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have become transformative technologies in financial risk assessment, offering novel methods to analyse, predict, and mitigate risks. Traditionally, financial institutions relied on statistical models and human judgment to assess credit, market, and operational risks. However, these methods often fell short in processing large-scale data or adapting to rapidly changing market conditions. AI and ML enhance this process by enabling the analysis of complex datasets, identifying hidden patterns, and providing predictive insights for risk management. Recent findings indicate that AI-driven models, such as neural networks and decision trees, outperform traditional models in accuracy and efficiency. These technologies allow for real-time risk analysis, dynamic pricing, fraud detection, and stress testing, enhancing decision-making and risk mitigation. AI's ability to adapt to new data without explicit programming further strengthens its role in evolving market environments. In conclusion, AI and ML have significantly reshaped financial risk assessment, offering enhanced predictive capabilities and automation. The future of financial risk management will likely see further integration of these technologies, with advances in explainable AI and real-time decision support systems. As these technologies evolve, financial institutions must continue to balance innovation with regulatory compliance, ensuring AI's ethical and transparent application in risk assessment.