Technological Innovation in Financial Fraud Detection: Evaluating Real-Time Monitoring Systems

Arun Kambhammettu ()

International Journal of Computing and Engineering, 2025, vol. 7, issue 11, 13 - 23

Abstract: Banking enterprises face unrelenting challenges from increasingly intricate deception strategies while working to preserve uninterrupted service delivery and customer satisfaction levels. The shift toward instantaneous monitoring marks a critical evolution in protective measures. This technological leap replaces outdated batch systems with real-time assessment mechanisms that identify questionable transactions during processing. Stream-based distributed platforms allow financial services to handle vast global transaction loads without performance degradation. Sophisticated algorithmic models continuously evaluate transaction elements against established behavior patterns, flagging irregularities within fractions of seconds. The strategic deployment of combined infrastructure—maintaining sensitive information locally while leveraging cloud resources for processing power—creates optimal operational balance. This responsive framework enables immediate threat recognition paired with automated defensive measures, including mobile-based verification steps. Measurable advantages manifest through reduced financial damages, enhanced precision in risk detection, and improved customer satisfaction ratings. The deliberate shift from response-based defenses to anticipatory protection mechanisms represents a crucial evolution in banking security strategies. These system innovations reveal how financial organizations can strengthen protective barriers, refine operational procedures, and enhance user experiences concurrently. The continuous monitoring infrastructure develops an adaptable platform for ongoing evolution against emerging deceptive methods while satisfying regulatory mandates and maintaining dependable service standards throughout the financial sector.

Keywords: Real-Time Fraud Detection; Banking Technology; Machine Learning; Distributed Streaming; Hybrid Cloud Architecture (search for similar items in EconPapers)
Date: 2025
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