Privacy-Preserving Ai Algorithms In Mobile Financial Services

Abstract

This article focuses on the rise of privacy-saving AI-based algorithms in mobile financial services and discusses their transformative effect on the industry, but it also raises an important issue for the internet that requires attention: data security. It examines four important technologies, including differential privacy, where calibrated noise is added to datasets to preserve statistical utility; homomorphic encryption, with the ability to operate computations directly on encrypted financial data; federated learning, which allows distributed model training across devices without centralizing sensitive data; and private cloud compute, which extends the protection of devices to cloud settings. The article deconstructs the application plans of the technologies through different financial institutions and, in the process, illustrates how the technologies help detect key frauds, offer personalized financial advice, deliver better credit risk analysis, and simplify regulatory compliance with high privacy standards. The article points out the use of these technologies by financial organizations to develop multi-layered security frameworks that secure the sensitive customer information across the lifecycle of AI and still allow innovation in financial services. Recently, alongside the growth of regulatory demands and increasing privacy concerns, these algorithms offer a technical basis for how responsible AI can be adopted within the more data-sensitive financial services industry.