A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems

• URL: http://arxiv.org/abs/2603.13237v1
• Date: Tue, 17 Feb 2026 10:20:40 GMT
• Title: A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems
• Abstract summary: High-frequency banking environments face a critical trade-off between low-latency fraud detection and regulatory explainability.<n>Traditional rule-based and discriminative models struggle with "zero-day" attacks due to extreme class imbalance and the lack of historical precedents.<n>This paper proposes a Dual-Path Generative Framework that decouples real-time anomaly detection from offline adversarial training.
• Score: 0.2864713389096699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-frequency banking environments face a critical trade-off between low-latency fraud detection and the regulatory explainability demanded by GDPR. Traditional rule-based and discriminative models struggle with "zero-day" attacks due to extreme class imbalance and the lack of historical precedents. This paper proposes a Dual-Path Generative Framework that decouples real-time anomaly detection from offline adversarial training. The architecture employs a Variational Autoencoder (VAE) to establish a legitimate transaction manifold based on reconstruction error, ensuring <50ms inference latency. In parallel, an asynchronous Wasserstein GAN with Gradient Penalty (WGAN-GP) synthesizes high-entropy fraudulent scenarios to stress-test the detection boundaries. Crucially, to address the non-differentiability of discrete banking data (e.g., Merchant Category Codes), we integrate a Gumbel-Softmax estimator. Furthermore, we introduce a trigger-based explainability mechanism where SHAP (Shapley Additive Explanations) is activated only for high-uncertainty transactions, reconciling the computational cost of XAI with real-time throughput requirements.

Related papers

• From Viral to Void: Multi-Dimensional Behavioral and Contractual Analysis for Rug Pull Identification [2.7875224007687827]
  Rug Pull scams are characterized by extremely low deployment costs, covert execution, rapid fund transfers, and high detection difficulty.<n>This paper develops a multi-dimensional feature system based on dimensions such as malicious smart contract design, on-chain transaction anomalies, liquidity manipulation, and social media disclosures.<n> Experiments show that the model achieves an accuracy of 0.927, an F1 score of 0.787, and an AUC-ROC of 0.952 on the test set, outperforming traditional methods.
  arXiv  Detail & Related papers  (2026-08-03T02:34:24Z)
• Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection [0.2864713389096699]
  We propose a multi-objective reinforcement learning framework to overcome class imbalance in financial anomaly detection.<n>Our approach synthesizes heterogeneous transaction features into cohesive natural-narratives, encoded by large language models.<n>It achieves superior minority-class recall compared to scalarized baselines, providing an alternative to trade bounded operational friction for financial anomaly discovery.
  arXiv  Detail & Related papers  (2026-07-10T17:39:00Z)
• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience [0.0]
  This paper proposes FraudShield AI, a hybrid framework that integrates Long Short-Term Memory (LSTM) networks with hand-crafted Graph Topological Features.<n>A Focal Loss objective is used to address class imbalance, and a dynamic thresholding mechanism is introduced to improve resilience against low-value smurfing attacks.
  arXiv  Detail & Related papers  (2026-05-02T02:10:20Z)
• GoAT-X: A Graph of Auditing Thoughts for Securing Token Transactions in Cross-Chain Contracts [52.51342355102833]
  Cross-chain bridges, the critical infrastructure of the multi-chain ecosystem, have become a primary target for attackers.<n>Existing defenses, such as bytecode-level static analysis, are ill-equipped to handle the semantic complexity of cross-chain interactions.<n>We propose GoAT-X, a framework that shifts automated cross-chain smart contracts auditing from pattern matching toward systematic first-principles verification.
  arXiv  Detail & Related papers  (2026-04-27T11:34:21Z)
• Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection [0.0]
  We propose Dynamic Sharded Learning (DSFL), a verifiable secure aggregation framework for fraud detection.<n>DSFL replaces topologies with Dynamic Sharding, reducing complexity from O(N2) to m(N2)<n>To mitigate insider threats, we introduce Linear Integrity Tags, an additive-homomorphic commitment mechanism.
  arXiv  Detail & Related papers  (2026-04-25T20:49:22Z)
• Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions [89.52532304099522]
  Federated Learning (FL) enables clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy.<n> gradients and model updates can still leak sensitive information, while malicious servers may mount adversarial attacks such as Byzantine manipulation.<n>These vulnerabilities highlight the need to address differential privacy (DP) and Byzantine robustness within a unified framework.<n>We propose Byz-Clip21-SGD2M, a new algorithm that integrates robust aggregation with double momentum and carefully designed clipping.
  arXiv  Detail & Related papers  (2026-03-24T17:39:09Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• Scalable and Reliable State-Aware Inference of High-Impact N-k Contingencies [4.588028371034407]
  Exhaustive evaluation of all outage combinations using AC power-flow or ACOPF is infeasible in routine operation.<n>This paper proposes a scalable, state-aware contingency inference framework designed to directly generate high-impact $N!-!k$ outage scenarios.
  arXiv  Detail & Related papers  (2026-02-10T06:55:59Z)
• Optimistic TEE-Rollups: A Hybrid Architecture for Scalable and Verifiable Generative AI Inference on Blockchain [4.254924788681319]
  We introduce Optimistic TEE-Rollups (OTR), a hybrid verification protocol that harmonizes constraints.<n>OTR achieves 99% of the throughput of centralized baselines with a marginal cost overhead of $0.07 per query.
  arXiv  Detail & Related papers  (2025-12-23T09:16:41Z)
• Ensemble of Precision-Recall Curve (PRC) Classification Trees with Autoencoders [3.060720241524644]
  Anomaly detection underpins critical applications from network security to intrusion detection to fraud prevention.<n>To combat the former, we previously introduced Precision-Recall Curve (PRC) classification trees and their ensemble extension, the PRC Random Forest (PRC-RF)<n>Building on that foundation, we now propose a hybrid framework that integrates PRC-RF with autoencoders, unsupervised machine learning methods that learn compact latent representations.
  arXiv  Detail & Related papers  (2025-09-06T16:39:22Z)
• HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling [0.0]
  Abnormal cryptocurrency transactions pose escalating risks to financial integrity.<n>Existing approaches are predominantly model-centric and post hoc.<n>This paper introduces HyPV-LEAD, a data-driven early-warning framework.
  arXiv  Detail & Related papers  (2025-09-03T12:23:38Z)
• Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns [0.0]
  This study presents a hybrid architecture for credit card fraud detection that integrates a Mixture of Experts (MoE) framework with Recurrent Neural Networks (RNNs), Transformer encoders, and Autoencoders.<n>MoE framework dynamically assigns predictive responsibility among the experts, enabling adaptive and context-sensitive decision-making.<n>The proposed hybrid system offers a scalable, modular, and regulation-aware approach to detecting increasingly sophisticated fraud patterns.
  arXiv  Detail & Related papers  (2025-04-01T20:47:18Z)
• A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection [60.09453163562244]
  We propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD.<n>In the estimation module, we design a novel label-free heterophily metric called HALO, which captures the critical graph properties for GFD.<n>In the alignment-based fraud detection module, we develop a joint-GNN architecture with ranking loss and asymmetric alignment loss.
  arXiv  Detail & Related papers  (2025-02-18T22:07:36Z)
• IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound [85.6899802468343]
  We present IBP-R, a novel verified training algorithm that is both simple effective. We also present UPB, a novel robustness based on $beta$-CROWN, that reduces the cost state-of-the-art branching algorithms.
  arXiv  Detail & Related papers  (2022-06-29T17:13:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.