Exploring the impact of artificial intelligence on financial risk prediction and management

The advent of artificial intelligence (AI) in financial risk prediction and management is transforming the nature of modern finance as new deep learning algorithms take center stage. This research delves into the application of advanced AI techniques, essentially deep learning, for an improvement in the accuracy and efficiency of models predicting risk. Conventional risk management techniques are incapable of processing the vast amounts of unstructured financial data available today. Unlike them, deep learning models, especially recurrent neural networks (RNNs) and convolutional neural networks (CNNs), can overcome most of the problems within financial data as well as capture complex patterns and correlations along with non-linear relationships. This paper is focused on deep learning’s potential in credit risk prediction volatility prediction and fraud detection through historical financial data, market sentiment, and external economic indicators. Effective combinations are predicted accuracy, real-time risk monitoring, and market changes, which were found to better conventional statistical methods when compared with AI-based models. Lastly, we talk about the challenges and ethical considerations that may arise from the implementation of AI in financial risk management. These may include algorithmic bias and transparency in decision-making processes. The findings of this research focus on the transformative capacity of AI, where both risk assessment and the proactive approach to risk mitigation are brought to the doorsteps of financial institutions in an increasingly volatile market environment.