Python-FRAUD-X: An Integrated AI, Blockchain, and Cybersecurity Framework-MyProjectBazzar

Online financial fraud remains a pervasive threat, incurring billions of dollars in global losses
annually. Mid-sized markets, such as North Macedonia, face acute challenges as digital adoption in the
Banking, Financial Services, and Insurance (BFSI) sector outpaces the establishment of robust, multi-layered
security systems. This paper introduces FRAUD-X, a unified framework merging artificial intelligence (AI)–
based anomaly detection, blockchain-driven transaction verification, cybersecurity intrusion detection, and
real-time early warning mechanisms into a single pipeline. Drawing upon three datasets—a Credit Card
Fraud dataset (Kaggle), the PaySim Mobile Money dataset, and collected 50,000 anonymized local BFSI
transactions from North Macedonia—FRAUD-X demonstrates a ∼2–4% improvement in F1 compared
to single-plane AI approaches, with ∼90% recall for zero-day threats. Key enhancements include: 1) a
permissioned blockchain for tamper-proof ledger entries, 2) synergistic AI–cybersecurity integration for
dynamic risk scoring, and 3) real-time alerts that reduce reaction windows from hours to mere minutes.
The framework runs at ∼15–16 ms per transaction (∼33% CPU usage), supporting near-real-time BFSI
operations. Ablation studies confirm that each synergy layer (blockchain, cybersecurity, and early warning)
significantly contributes to overall performance. A security analysis illustrates how FRAUD-X mitigates
node compromise, collusion attempts, and advanced persistent threats (APT). By providing a replicable
roadmap that balances high detection accuracy with operational feasibility, FRAUD-X
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