V3I2P51

Effectiveness of AI based fraud detection in UPI transaction

Anagha V K^1*, Jagannadhan S²

Abstract

Despite its swift growth, India’s Unified Payments Interface now faces rising threats even as it reshapes daily money transfers between people and companies. Instead of slowing down, scams like fake messages, stolen identities, altered payment codes, and manipulation through deception have grown alongside adoption rates. A closer look at artificial intelligence appears in this paper – named “Effectiveness of AI-Based Fraud Detection in UPI Transactions” – to assess whether smart systems can catch suspicious activity faster than older methods do. From spotting odd patterns to cutting down on lost funds, machine learning may quietly help users feel more confident when paying online. A closer look at how people view AI-driven fraud prevention begins with gathering answers from 150 individuals using UPI services. Because responses came via a fixed-format survey, insights emerge on awareness levels alongside feelings of safety and reliance on automated safeguards. Information beyond firsthand replies comes into play too – documents issued by the Reserve Bank of India, materials from NPCI, scholarly articles, and investigations within fintech shape context around rising digital scams and machine learning use in banking defense. When it comes to making sense of numbers, methods like proportional breakdowns, visual graphs, and side-by-side evaluations highlight tendencies tied to scam encounters, instant warning signals, precision in identifying threats, incorrect flags, and faith in system performance. Patterns take form not just through volume but through contrast and repetition across sources.Most people who took part in the survey view AI-powered fraud detection tools as more capable than older rule-driven methods. Real-time tracking, automatic warnings, and quicker spotting of unusual behavior helped boost trust among users. Yet doubts linger – some worry about personal information being exposed, legitimate payments getting wrongly stopped, or simply not knowing how these smart systems actually work. Even though artificial intelligence clearly strengthens UPI safety layers, lasting impact will rely on clearer system logic, fewer incorrect flags, better public understanding, and stronger safeguards for private data. These outcomes matter greatly for financial institutions, tech firms, oversight bodies, and scholars aiming to shape a reliable digital money landscape across India.

Keywords:

Artificial Intelligence (AI), UPI Fraud Detection, Digital Payment Security, Machine Learning in Finance, Real-Time Transaction Monitoring