Project Management for Anti-Fraud Platforms in Financial Institutions: Integrating AI, Real-Time Alerts, and Compliance Automation
DOI:
https://doi.org/10.15662/IJARCST.2023.0603005Keywords:
AI-Driven Fraud Detection, Real-Time Alerting Systems, Compliance Automation, Project Management Framework, Financial Crime Analytics, Model Risk Governance, Network-Based Fraud DetectionAbstract
Financial institutions are facing unprecedented levels of fraud risk driven by increasing digital transaction volumes, rapid adoption of mobile banking, complex international payment systems, and evolving cybercrime tactics. Fraud schemes have become more sophisticated, leveraging social engineering, mule networks, synthetic identities, and automated attack platforms capable of bypassing traditional rule-based detection engines. In this environment, anti-fraud platforms must transform from static, reactive systems into dynamic, intelligence-driven ecosystems capable of real-time pattern recognition, predictive analytics, and automated compliance alignment. This research presents an integrated project management and governance framework for implementing AI-centric anti-fraud platforms within global financial institutions. The framework unifies machine learning models, real-time alerting systems, case investigation workflows, sanctions screening processes, and regulatory reporting controls into a single cohesive architecture.The study evaluates anti-fraud transformation programs across 26 financial institutions between 2018 and 2024, analyzing quantitative outcomes pertaining to detection accuracy, alert reduction, investigation efficiency, operational costs, and regulatory compliance metrics. The results demonstrate that institutions adopting AI-driven anti-fraud platforms achieved a 61% improvement in detection accuracy, 48% reduction in false positives, and 72% faster investigation turnaround. The project management model introduced in this article outlines the lifecycle for planning, building, validating, deploying, and governing these systems, emphasizing cross-functional coordination, data governance, model risk oversight, and compliance automation. Four integrated figures visualize the platform architecture, real-time detection flow, predictive analytics engine, and enterprise-wide operating model. The findings confirm that integrating AI, real-time alerts, and compliance automation significantly strengthens fraud resilience while reducing operational burdens on compliance teams.
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