AI-Based Predictive Analytics for Financial Risk Management

Financial risk management is crucial in safeguarding the stability and security of financial institutions, particularly in an era marked by fast technological break-throughs. This research analyzes the usefulness of sophisticated deep learning algorithms for forecasting financial hazards. We adopted a comparison technique utilizing neural networks (NN) and convolutional neural networks (CNN) to examine their performance in risk prediction. There are 490 examples in the dataset with the feature engineering majoring in the financial ratios and dangers. Python-based frameworks such as TensorFlow, Keras, and Scikit-learn were utilized in the development and testing of the models. The assessment criteria comprised accuracy, precision, recall, F1 score, and the ROC-AVC curve to ensure an overall evaluation of the created models. The findings reported above demonstrate that the CNN model gave better outcomes than the NN model with an accuracy of 94%. 5% as well as the AUC were 0.97 against 91, which is the accuracy of the NN model. 2%, and the AUC was 0.92. These data suggest that CNN has greater capacity for collecting complicated patterns and boosting the risk prediction accu-racy. According to this paper, it is suggested that there are higher prospects of executing financial risk management with enhanced deep learning algorithms, which bring huge improvements to earlier techniques. From the findings, it can be stated that adding such AI-based models might be advantageous to financial organizations as it boosts the risk assessment and decision-making operations. Further developments in these models should be researched in future studies, and their generalizability to different financial environments should be evaluated.