Engineering an AI-Driven Financial Risk Assessment Platform Using Temporal Modeling and Cross-Source Economic Data Fusion

Abstract

Complexity of the financial markets and the emergence of many and disparate sources of economic information require risk assessment platforms with rich features used to compute temporal dependencies, pre-processed information fusion, etc. In this paper, a new type of AI supported financial risk evaluation tool is presented that uses temporal modeling and fusion of cross-sources in economic data to extend risk prediction accuracy and simultaneous decision-making properties. Our suggested framework will combine Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and multi-modal data fusion architecture that will process various financial indicators such as market facts, economic indicators, news sentiment, and information about regulation. Empirical testing on actual financial data 2020-2024 with real financial data shows that the risk prediction accuracy is dramatically enhanced by 35% and 28 percent more efficient in detecting and early warning on risks than conventional methods of risk assessments. The platform can execute more than 10M transactions in a day with sub-second latency and thus can be used in high frequency trading systems and real-time regulatory compliance surveillance systems.