The Power of Personalized Finance: Harnessing the Potential of Machine Learning in Hyper-Personalized Banking

Abstract The incorporation of machine learning (ML) into personalized finance is a paradigm shift for financial services and represents a major leap forward for financial institutions by providing hyper-personalized banking solutions using algorithmic intelligence. This study examines the transformative potential of ML-driven personalization for credit assessment, fraud detection, and risk management. By providing a holistic view of behavioral data, analysis of transactional patterns, and tapping into alternative data, ML offers unique and unprecedented hyper-personalized finance in real-time at scale. This study provides a systematic analysis of ML across the lending process, from pricing, fraud detection, and microfinance, while addressing the significant issues that institutions face concerning infrastructure, model explainability, and regulatory oversight. The study introduced a new six-stage maturity and capacity framework intended to provide a reference to institutions as they learn to adopt ML for personalization, with a description of the governance and ethical considerations at each maturity level. ML and personalized finance resulted in notable gains in many aspects including 45 percent better credit accessibility for vulnerable population, 92 percent accuracy in fraud detection, and 80 percent improved operational time efficiencies. The research asseverated that ML and personalized finance led to improvements in financial inclusion, enhanced customer experience, and improved trust in the digital banking ecosystem. Keywords Machine learning, personalized finance, credit scoring, fintech, fraud detection, risk-based pricing, MLOps, AI governance, explainable AI, financial inclusion