Personalized Financial Services through Artificial Intelligence: Transforming FinTech with Predictive Analytics and Deep Learning

Using artificial intelligence, deep learning, and graph-based methodologies, this research developed a framework for personalizing FinTech applications. The goal of this framework is to improve the accuracy of forecasts, risk ranking, and user experience. This approach was developed to measure a wide range of financial behaviors in a fair and equal manner. Some examples of these behaviors include how people’s spending habits, portfolio growth, and credit risk differ from one another. It was necessary to develop data that was both synthetic and statistically credible to validate performance in real-world conditions. In terms of prediction accuracy, area under the curve (AUC) scores, retention probability, and suggestion acceptance rates, the data revealed that the presented models performed better than traditional approaches. In addition, the framework has demonstrated that it is capable of being utilized on a vast scale with minimal latency, which makes it an excellent option for widespread application. This research gained an understanding of the disruptive nature of artificial intelligence in personalized financial services and demonstrated the tactical utility of XAI for sustainable fintech innovation.