ENHANCING DIGITAL TRANSACTIONS: THE POWER OF PREDICTIVE ANALYTICS IN UPI APPS FOR IMPROVED SECURITY AND USER EXPERIENCE

ABSTRACT The rapid adoption of Unified Payments Interface (UPI) applications has significantly transformed the digital payments landscape, offering unparalleled convenience and user engagement. However, this rapid growth has also raised concerns regarding user experience management and security at scale. This paper examines the potential benefits of predictive analytics on customer behavior within UPI applications by conducting an indepth survey of users from many different demographics. We want to bring to light the user's behavioral tendencies, security crises and the fintech sector growth patterns, by means of the behavioral data and the users' demographic details. Our study is mainly on machine learning and data analytics which are used in converting transaction data to consumer behavior predicting, improving the personalization system, supporting fraud detection, and creating a more secure financial environment. The literature review highlights advancements in predictive analytics within fintech and outlines future research directions, particularly the integration of machine learning into UPI applications. We also address the ethical implications of predictive analytics, including data protection and user consent. By integrating data science with fintech, we propose how predictive models can drive more customized and secure UPI applications, ultimately enhancing user engagement and financial safety. Keywords: Unified Payments Interface (UPI), Predictive Analytics, Consumer Behavior, Fintech Security, Machine Learning, Data Science, Fraud Detection, User Experience, Financial Technology