TITLE:
Risk-Aware AI Models for Financial Fraud Detection: Scalable Inference from Big Transactional Data
AUTHORS:
Utham Kumar Anugula Sethupathy
KEYWORDS:
Financial Fraud, Artificial Intelligence, Hybrid Models, Risk Profiling, Scalable Inference, SHAP, Explainable AI, Anomaly Detection, Real-Time Fraud Detection, Feature Engineering, Model Deployment
JOURNAL NAME:
International Journal of Intelligence Science,
Vol.15 No.4,
September
3,
2025
ABSTRACT: Purpose: The purpose of this study is to develop a scalable, risk-aware artificial intelligence (AI) framework capable of detecting financial fraud in high-throughput digital transaction environments. The research addresses the limitations of traditional rule-based and single-model systems, which often suffer from high false positives, poor adaptability, and limited interpretability. By combining supervised machine learning, unsupervised anomaly detection, and engineered domain-specific risk features, the proposed hybrid architecture aims to enhance precision, recall, and transparency. The framework is designed to meet stringent regulatory and operational requirements, making it deployable across banks, fintechs, digital wallets, and other institutions handling large volumes of transactions. Design/Methodology/Approach: The framework integrates a modular, five-layer architecture: 1) real-time multi-source data ingestion, 2) domain-driven risk feature engineering, 3) hybrid AI inference combining XGBoost and autoencoder models, 4) interpretability via SHAP and counterfactual reasoning, and 5) seamless decisioning and integration with downstream fraud operations. Distributed cloud-native infrastructure using Kafka, Spark, Kubernetes, and ONNX Runtime ensures scalability and low latency. Training leverages imbalanced financial datasets using advanced sampling techniques and time-series aware cross-validation. Continuous monitoring for data drift, fairness, and explainability supports compliance. Experimental validation was conducted using over 100 million anonymized transactions from multinational banking partners under production-like conditions. Findings: The hybrid framework achieved significant improvements over legacy systems and single-model baselines. Precision, recall, and F1-score reached 0.94, 0.93, and 0.935, respectively, outperforming rule-based approaches by over 35%. Median inference latency was reduced to 126 ms under 10,000 transactions per second, supporting real-time operations. SHAP-based interpretability and counterfactual reasoning provided transaction-level explanations, meeting regulatory transparency requirements. Top-1% alerting achieved a 92% fraud hit rate, reducing analyst workload. Data drift monitoring maintained model stability for over 90 days post-deployment. These findings demonstrate that hybrid AI designs can balance speed, accuracy, scalability, and auditability for modern fraud detection. Research Limitations/Implications: The study’s evaluation relied on datasets from two multinational banking partners, which, while extensive, may not fully capture fraud patterns across all geographies or payment ecosystems. Although the hybrid model effectively detected emerging threats, its performance depends on the quality and representativeness of input data, and it may require tuning for domain-specific deployment. Graph-based collusion detection and federated learning across institutions were not implemented, limiting collaborative intelligence. Future research should focus on enhancing adversarial robustness, cross-institution data sharing with privacy guarantees, and dynamic adaptation to evolving fraud tactics. Broader validations could further generalize the model’s applicability across diverse markets. Practical Implications: Financial institutions can deploy this hybrid AI framework to improve fraud detection accuracy and reduce false positives, thereby enhancing customer trust and operational efficiency. The modular design supports integration with existing fraud operations, SIEM, and payment systems. Real-time interpretability enables faster analyst decision-making and ensures compliance with GDPR, PSD2, and PCI-DSS requirements. Scalable cloud-native infrastructure allows institutions to handle transaction volumes exceeding 100 million daily. Reducing analyst workload through high Top-K fraud detection effectiveness can lower operational costs. The approach provides a production-ready blueprint for banks, fintechs, and payment processors seeking to modernize their fraud prevention capabilities. Social Implications: By reducing fraud losses, this framework strengthens the stability of the global financial ecosystem and protects consumers from identity theft and unauthorized transactions. Enhanced fraud detection minimizes the reputational damage and operational inefficiencies caused by false positives, improving trust in digital banking and payment systems. Regulatory compliance and transparency features align with data protection laws, ensuring ethical and responsible AI use. Broader adoption could deter criminal networks by increasing detection rates and reducing avenues for financial exploitation. The approach ultimately contributes to a safer digital economy, promoting financial inclusion and resilience in both emerging and established markets. Originality/Value: This study is the first to present a fully production-ready, hybrid AI fraud detection architecture that balances scalability, interpretability, and regulatory readiness. Unlike prior work that focused on isolated algorithms, the framework unifies supervised learning, unsupervised anomaly detection, and engineered risk scoring within a modular, cloud-native infrastructure. The inclusion of SHAP-based explanations and counterfactual reasoning directly addresses the “black-box” issue in financial AI systems. Empirical validation on large-scale real-world data demonstrates substantial performance and latency improvements. The framework offers a repeatable blueprint for institutions to deploy advanced, explainable fraud detection systems capable of evolving with dynamic global payment ecosystems.