Smart Device for Predictive Risk Management in Investments with Machine Learning

Abstract

AI and ML technologies now significantly enhance risk management analytics, resulting in a notable transformation in financial management practices. This study examines the use of AI/ML in mitigating credit, market, and operational risk, as well as in stress testing and scenario analysis. Utilizing AI/ML enables financial organizations to analyze diverse inputs, identify complex patterns, and provide precise forecasts, therefore enhancing decision-making and optimizing operations.
In credit risk, AI solutions effectively use data not typically associated with credit scoring, allowing the sector to inclusively accommodate all individuals, hence eliminating biases often present in conventional methods. In the context of market risks, the use of AI/ML employs tools to identify deviations and forecast potential changes promptly, hence mitigating losses. Operational risks are handled by efficient operational line management and compliance achieved using machine learning-driven, evidence-based, proactive solutions that minimize bureaucracy and enhance governance. Furthermore, stress testing models using AI/ML produce simulated situations to develop superior solutions that boost financial robustness.              
This study examines the challenges posed by these technologies, including ethical difficulties, data privacy concerns, and the repetitive nature of models. The research highlights the suitability of AI/ML in financial risk management for accuracy, cost-efficiency, and a strategic perspective on comprehensive risk management. This study establishes a significant foundation for further investigations into innovative AI/ML technologies and their implications for financial resilience and ethical risk management.