Trustworthy and Adversarial-Resilient AI for Financial Crime Defense: A Unified Framework for Fraud Detection, Anti-Money Laundering, Credit Risk, and Secure Payments

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@article{1722714,
      author = {Rohit Jadav, Raj Kumar Mishra, Pankaj Rai},
      title = {Trustworthy and Adversarial-Resilient AI for Financial Crime Defense: A Unified Framework for Fraud Detection, Anti-Money Laundering, Credit Risk, and Secure Payments},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {218-225},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722714.pdf},
      abstract = {Financial institutions face an increasingly interconnected set of threats spanning transaction fraud, money laundering, adversarial manipulation of scoring models, payment-system exploitation, and network-level intrusions against the infrastructure that hosts these services. Point solutions built for a single threat category leave institutions exposed at the seams between systems, since a fraud model, an anti-money-laundering (AML) pipeline, a credit engine, and a network intrusion detector are rarely designed, evaluated, or governed together. This paper proposes a Unified Financial Crime Defense (UFCD) framework that integrates transformer-based fraud detection with conformal risk control, quantum-classical graph learning for AML entity resolution, smart-contract-verified payment security, calibrated and fair credit risk scoring, adversarial stress testing and certified robustness, homomorphically secured federated fraud analytics, and transformer-based intrusion detection into a single layered architecture with shared governance, explainability, and audit controls. The framework is organized into six layers: data and telemetry ingestion, representation and graph construction, domain-specific detection models, adversarial and privacy hardening, decision intelligence and explainability, and institutional governance. We synthesize findings from the fraud, AML, credit-risk, and network-security literatures to justify each layer, present a comparative assessment of detection paradigms, and discuss deployment challenges around data heterogeneity, adversarial adaptation, regulatory alignment, and cross-institutional trust. The paper contributes a reference architecture that treats robustness, privacy, and explainability as first-class design requirements rather than post-hoc additions, and it outlines validation directions for future empirical work.},
      keywords = {fraud detection, anti-money laundering, adversarial robustness, federated learning, graph neural networks, credit risk, intrusion detection, financial cybersecurity, explainable ai.},
      month = {September},
      doi = {https://doi.org/10.64388/IREV10I3-1722714}
  }