AI Based Portfolio Optimization and Customer Risk Profiling in Fintech Platforms

Abstract

This research proposes an AI-based approach to improve portfolio optimization and customer risk profiling in FinTech investment platforms. Demand for smarter and more adaptable methods stems from the inability of traditional approaches to effectively manage investor activity and market variation. We created and tested a dual-engine system that combines reinforcement learning strategies and classification models for user risk profiling and integrated machine learning algorithms for behavioral segmentation, portfolio allocation, and risk personalization. The methodology uses real-world data from users of FinTech portals, emulates different market environments, and measures the performance of the AI system compared to traditional techniques of portfolio optimization. The analysis reveals performance enhancements in return-to-risk ratio, accuracy of risk classification, and level of diversification across customer segments. This research illustrates the range of automated tailored financial services powered by AI, and provides guidance on applying programmable, flexible investment advisory services in digital finance ecosystems.