AI in Risk Assessment and Management in Finance

Abstract

The main contribution of core financial discipline, for instance, risk assessment and management to financial stability is for example to determine decisions, credit evaluation and fraud detection in the investment. As application of advanced artificial intelligence (AI) for achieving higher accuracy and efficiency in risk prediction in the financial market demand is growing, it is needed to work on it. In this research, we used the application of deep neural networks as an ‘advanced AI’ such that it would help manage and assess financial risk. Another application of DNNs is its use in the prediction process in credit scoring, portfolio optimization, and anomaly detection due to their good performance in identifying fine patterns in gigantic financial datasets leading to improved accuracy in the prediction. This research can be performed using the widely used AI tool TensorFlow as it is scalable, equipped with all the deep learning skills, and already implemented. Integration of TensorFlow to the operation of large scale data and of proactive detection of risks by financial institutions using Deep Neural Networks (DNNs). As a result, a gain in terms of classifications regulation compliance and a reduced loss of money are obtained by leveraging the AI based intelligence to boost watchfulness on the financial edge. AI’s impact on financial risk management is transformational, can be based on data, and can constitute a data driven, resiliant basis for financial strategies.