Application of Intelligent Algorithms in the Construction of Financial Data Privacy Protection and Risk Assessment Models

In the context of rapid development of informatization, the security and privacy protection of financial data have become important issues that urgently need to be solved. This article intends to apply intelligent algorithms into the privacy protection and risk assessment model of financial data, seeking a balance point that can ensure both personal privacy and efficient use of financial data and risk management. This article used methods such as machine learning and deep learning to model and evaluate financial data. In homomorphic encryption performance testing, encryption, decryption, and computation time were all increasing, with the highest computation time being 240.3 seconds and the highest decryption time being 81.2 seconds. The research results of this article have greatly improved the efficiency and accuracy of financial data processing, and effectively guaranteed data security and service quality in the financial field by strengthening data encryption and anonymity processing.