Securing Instant Payments: Implementing Fraud Prevention Frameworks with AVS and OTP Validation

The fast development of instant-payment systems within the FinTech environment is both convenient and increases the risk of fraud, which necessitates the implementation of effective preventive systems. The paper describes the creation, deployment, and testing of a fraud-prevention system, using Address Verification Service (AVS) and One-Time Password (OTP) validation as a part of the large-scale instant-payment infrastructure based on a modern stack of Scala, TypeScript, GraphQL, BigQuery, Kafka, and Google Cloud Platform (GCP) and product design in Figma and project management in Jira. The suggested approach applies cross-service coordination of microservices to screen transactions in real-time: AVS verification microservice verifies billing address/cardholder address matches, OTP microservice issues and validates SMS/email OTPs, fraud-analytics pipeline aggregates transaction metadata (device, geolocation, velocity) in BigQuery to be risk-scored; a GraphQL API coordinates the services.In evaluating system performance, the solution achieved a measurable decrease in instant-transfer loss rate driven by multilayered validations, including card declination rates exceeding 20 percent of transactions, AVS and name-matching validation declines of approximately 10 percent, and BIN-based declines of roughly 3 percent. These safeguards collectively contributed to an overall revenue increase of about 5 percent while maintaining transaction latency within the acceptable SLA range of 180–225 ms. We also break down performance based on risk segments, with performance being maximally beneficial in first-time payers and domestic transactions. The results confirm that an address-verification and a layered, real-time authentication approach built into a scalable cloud-native stack can significantly increase the level of security in an instant-payment-based setting without being excessively detrimental in terms of usability.