Design and Implementation of an AI-Driven Hybrid Framework for Risk Assessment

Abstract

This paper discusses key challenges of data processing in the field of artificial intelligence (AI), specifically in dealing with unstructured data and adapting to market changes. We propose a novel AI risk assessment framework by developing a multi-model hybrid scoring system that integrates machine learning and deep learning, focusing on random forests and long Short-Term memory (LSTM) networks. Experimental validation shows that our framework performs more effectively in accurate risk classification compared with existing SOTA methods, significantly enhancing the capabilities of AI systems in complex data environments. Our results provide a new perspective and technical approach for AI risk assessment, which plays a crucial role in the optimization and application of AI systems.

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