Shapley Value-Guided Adaptive Ensemble Learning for Explainable Financial Fraud Detection with U.S. Regulatory Compliance Validation

• URL: http://arxiv.org/abs/2604.14231v1
• Date: Tue, 14 Apr 2026 19:00:20 GMT
• Title: Shapley Value-Guided Adaptive Ensemble Learning for Explainable Financial Fraud Detection with U.S. Regulatory Compliance Validation
• Abstract summary: Financial crime costs U.S. institutions over $32 billion each year.<n>Many AI tools for fraud detection operate as black boxes that cannot provide transparent, auditable explanations.<n>This study offers a thorough evaluation of explanation quality across faithfulness and stability.
• Score: 0.34376560669160394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Financial crime costs U.S. institutions over $32 billion each year. Although AI tools for fraud detection have become more advanced, their use in real-world systems still faces a major obstacle: many of these models operate as black boxes that cannot provide the transparent, auditable explanations required by regulations such as OCC Bulletin 2011-12 and Federal Reserve SR 11-7. This study makes three main contributions. First, it offers a thorough evaluation of explanation quality across faithfulness (sufficiency and comprehensiveness at k=5, 10, and 15) and stability (Kendall's W across 30 bootstrap samples). XGBoost paired with TreeExplainer achieves near-perfect stability (W=0.9912), while LSTM with DeepExplainer shows weak results (W=0.4962). Second, the paper introduces the SHAP-Guided Adaptive Ensemble (SGAE), which dynamically adjusts per-transaction ensemble weights based on SHAP attribution agreement, achieving the highest AUC-ROC among all tested models (0.8837 held-out; 0.9245 cross-validation). Third, a complete three-architecture evaluation of LSTM, Transformer, and GNN-GraphSAGE on the full 590,540-transaction IEEE-CIS dataset is provided, with GNN-GraphSAGE achieving AUC-ROC 0.9248 and F1=0.6013. All results are mapped directly to OCC, SR 11-7, and BSA-AML regulatory compliance requirements.

Related papers

• Post-Operative Glioma Segmentation via Loss Stabilization, Normalization and Subspace Attention [46.03321798937855]
  We show that the standard Generalized Dice Loss (GDL) is unstable under domain shift.<n>We propose a Subspace-Aware Class Attention (SACA) module that re-calibrates the bottleneck features and raises Enhancing Tumor (ET) sensitivity by 8%.
  arXiv  Detail & Related papers  (2026-07-23T09:59:30Z)
• Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems [47.027290803102666]
  Normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes.<n>We model the normal law with a jointly learned latent representation plus explicit Gaussian-mixture mode clustering.<n>We evaluate under a deliberately fair protocol: raw point-wise metrics with no point adjustment, a trivial-detector difficulty split, prevalence-matched F1, and train-normal-only calibration.
  arXiv  Detail & Related papers  (2026-07-07T10:10:00Z)
• Validation-Stage Combinatorial Fusion Analysis for Imbalanced Credit-Card Fraud Detection [8.713333739961403]
  This paper examines whether Combinatorial Fusion Analysis (CFA) can still add value on the IEEE-CIS Fraud Detection benchmark.<n>CFA matches soft voting on AUC-ROC, improves AUPRC and F1, and outperforms gradient stacking in this setting.
  arXiv  Detail & Related papers  (2026-06-09T04:14:59Z)
• Federated Naive Bayes with Real Mixture of Gaussians and Institutional Governance Regularization for Network Intrusion Detection [0.0]
  Federated learning for intrusion detection rests on a flawed premise: that every participating institution contributes equally to the shared model.<n>In practice, a financial institution with mature security controls and low vulnerability exposure produces fundamentally different data than a government agency running with weaker controls and higher vulnerability exposure.<n>Treating their local models as models discard information that organisations already collect through standard risk management audits.
  arXiv  Detail & Related papers  (2026-05-18T16:54:49Z)
• ComplianceNLP: Knowledge-Graph-Augmented RAG for Multi-Framework Regulatory Gap Detection [13.891522069967507]
  Financial institutions track over 60,000 regulatory events annually.<n>The industry has paid over USD 300 billion in fines and settlements since the 2008 financial crisis.<n>We present ComplianceNLP, an end-to-end system that automatically monitors regulatory changes, extracts structured obligations, and identifies compliance gaps.
  arXiv  Detail & Related papers  (2026-04-26T07:44:50Z)
• A Regulatory Governance Framework for AI-Driven Financial Fraud Detection in U.S. Banking: Integrating OCC, SR 11-7, CFPB, and FinCEN Compliance Requirements for Model Development, Validation, and Monitoring Lifecycles [0.40611352512781873]
  This paper presents the Regulatory Governance Framework for AI-Driven Financial Fraud Detection (RGF-AFFD)<n>It is a three-tier governance architecture empirically anchored in a multi-study empirical program.<n>The RGF-AFFD is the first integrated deployment blueprint to simultaneously satisfy OCC, SR 11-7, CFPB, and FinCEN requirements.
  arXiv  Detail & Related papers  (2026-04-14T08:30:04Z)
• When Self-Reference Fails to Close: Matrix-Level Dynamics in Large Language Models [0.0]
  Self-referential statements and meta-cognitive prompts are more stable than paradoxical self-reference on key collapse-related metrics.<n>We propose, as a conjecture, that NCTR forces finite-depth transformers toward dynamical regimes where these problems concentrate.
  arXiv  Detail & Related papers  (2026-04-13T23:23:02Z)
• How Independent are Large Language Models? A Statistical Framework for Auditing Behavioral Entanglement and Reweighting Verifier Ensembles [46.63622714488747]
  Shared pretraining data, distillation, and alignment pipelines can induce hidden behavioral dependencies, latent entanglement.<n>In practice, this manifests as correlated reasoning patterns and synchronized failures.<n>We develop a statistical framework for auditing behavioral entanglement among black-box language models.
  arXiv  Detail & Related papers  (2026-04-08T23:32:06Z)
• Deterministic Fuzzy Triage for Legal Compliance Classification and Evidence Retrieval [0.0]
  Legal teams increasingly use machine learning to triage large volumes of contractual evidence.<n>Many models are opaque, non-deterministic, and difficult to align with frameworks such as HIPAA or NERC-CIP.<n>We study a simple, reproducible alternative based on deterministic dual encoders and transparent fuzzy triage bands.
  arXiv  Detail & Related papers  (2026-03-08T00:31:34Z)
• Robust and Real-Time Bangladeshi Currency Recognition: A Dual-Stream MobileNet and EfficientNet Approach [2.3053825622580133]
  We build a new Bangladeshi banknote dataset that includes both controlled and real-world scenarios.<n>We incorporate four additional datasets, including public benchmarks, to cover various complexities and improve the model's generalization.<n>The proposed model achieves 97.95% accuracy on controlled datasets, 92.84% on complex backgrounds, and 94.98% accuracy when combining all datasets.
  arXiv  Detail & Related papers  (2026-01-31T17:37:16Z)
• CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning [57.24524263804788]
  Code verifiers play a critical role in post-verification for LLM-based code generation.<n>Existing supervised fine-tuning methods suffer from data scarcity, high failure rates, and poor inference efficiency.<n>We show that naive RL with only functionality rewards fails to generate effective unit tests for difficult branches and samples.
  arXiv  Detail & Related papers  (2026-01-30T10:33:29Z)
• Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents [0.7699235580548228]
  LLM agents struggle with regulatory audit replay: when asked to reproduce a transaction flagged decision with identical inputs, most deployments fail to return consistent results.<n>This paper introduces the DeterminismFaithfulness Assurance Harness (DFAH), a framework for measuring trajectory determinism and evidence-conditioned faithfulness in tool-using agents deployed in financial services.
  arXiv  Detail & Related papers  (2026-01-17T19:47:55Z)
• Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models [45.21399037022976]
  CausalMixFT is a method that enhances fine-tuning robustness and downstream performance.<n>It generates structurally consistent synthetic samples using Structural Causal Models (SCMs) fitted on the target dataset.<n> evaluated across 33 classification datasets from TabArena and over 2300 fine-tuning runs.
  arXiv  Detail & Related papers  (2026-01-07T17:16:39Z)
• Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving [65.02106674311908]
  This paper introduces Intern-S1-MO, a long-horizon math agent that conducts multi-round hierarchical reasoning.<n>By maintaining a compact memory in the form of lemmas, Intern-S1-MO can more freely explore the lemma-rich reasoning spaces.<n> Experiments show that Intern-S1-MO can obtain 26 out of 35 points on the non-geometry problems of IMO2025, matching the performance of silver medalists.
  arXiv  Detail & Related papers  (2025-12-11T15:26:28Z)
• LLM Output Drift: Cross-Provider Validation & Mitigation for Financial Workflows [0.5798758080057375]
  Nondeterministic outputs (output drift) undermine auditability and trust.<n>We quantify drift across five model architectures on regulated financial tasks.<n>This finding challenges conventional assumptions that larger models are universally superior for production deployment.
  arXiv  Detail & Related papers  (2025-11-10T19:54:00Z)
• CoT-Saliency: Unified Chain-of-Thought Reasoning for Heterogeneous Saliency Tasks [96.64597365827046]
  We present the first unified framework that jointly handles three operationally heterogeneous saliency tasks.<n>We introduce a Chain-of-Thought (CoT) reasoning process in a Vision-Language Model (VLM) to bridge task heterogeneity.<n>We show our model matches or outperforms specialized SOTA methods and strong closed-source VLMs across all tasks.
  arXiv  Detail & Related papers  (2025-11-01T04:37:01Z)
• CUARewardBench: A Benchmark for Evaluating Reward Models on Computer-using Agent [46.41047559759938]
  Computer-using agents (CUAs) enable task completion through natural interaction with operating systems and software interfaces.<n> Reward models offer promising alternatives, but their effectiveness on CUA evaluation remains largely underexplored.<n>We present CUARewardBench, comprising four key contributions.
  arXiv  Detail & Related papers  (2025-10-21T12:53:40Z)
• Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity [55.29408396918968]
  We study a family of loss functions named label-distributionally robust (LDR) losses for multi-class classification. Our contributions include both consistency and robustness by establishing top-$k$ consistency of LDR losses for multi-class classification. We propose a new adaptive LDR loss that automatically adapts the individualized temperature parameter to the noise degree of class label of each instance.
  arXiv  Detail & Related papers  (2021-12-30T00:27:30Z)

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.