An Explainable Artificial Intelligence (XAI) framework tailored for banking transaction anomaly detection within internal audit workflows is introduced, confirming that feature-level explanations measurably improve auditor confidence and decision quality.
Abstract
The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approaches struggle with high false-positive rates and offer no justification for their outputs, limiting their utility for compliance teams. This paper introduces an Explainable Artificial Intelligence (XAI) framework tailored for banking transaction anomaly detection within internal audit workflows. An Isolation Forest (iForest) model performs unsupervised anomaly scoring, while a SHAP (SHapley Additive exPlanations) layer provides transaction-level, feature-attributed explanations grounded in cooperative game theory [8]. A lightweight Streamlit dashboard renders these outputs in a form accessible to audit professionals without machine learning expertise. Evaluation on a synthetic banking dataset yields 0.91 precision and 0.88 recall, outperforming three unsupervised baselines. Expert feedback confirms that feature-level explanations measurably improve auditor confidence and decision quality. The framework advances the practical deployment of accountable, transparent AI in regulated financial environments.
The typical approach to fraud detection in retail environments relies on rules-based, static detection engines. Such systems perform well against simple, known attack types but struggle against sophisticated, emerging threats and organized financial syndicates. Moreover, rules-based systems produce high false positive rates that seriously degrade the experience of legitimate customers. This paper presents a Graph Artificial Intelligence framework to support high-throughput and streaming retail transactions with built-in explainability. Our approach treats transactions as a dynamic and heterogeneous graph to capture complex relational dependencies and structural anomalies in real time. We validate the framework using a curated subset of the publicly available IEEE-CIS Fraud Detection dataset under a streaming scenario, evaluating both latency and predictive accuracy. Combined with PyTorch Geometric for graph neural network construction and SHAP (Shapley Additive exPlanations) for post-hoc model interpretation, our system delivers high prediction accuracy and human-interpretable decision making. The proposed model shows strong accuracy and recall compared to traditional baselines, while flagged transactions are explained through visual subgraph evidence. This transparency enables fraud analysts to validate alerts efficiently, reducing review time and operational cost in e-commerce environments.
Jose Prabhu Michael Singarayan, Jayakumar Ramalingam, Neetu Uthaman· 2026 International Conferenc...· 0 citations
The evaluation of accounting transactions is increasingly challenging due to the growing volume of financial records, severe class imbalance, and the limited transparency of existing audit support systems. Many current machine learning approaches emphasize prediction accuracy while providing insufficient interpretability and weak support for risk-oriented audit decisions. To address these issues, this paper proposes an Intelligent Accounting framework based on explainable machine learning for risk-oriented transaction outcome prediction. The proposed framework integrates accounting-driven feature engineering, supervised learning, SHAP based explainable artificial intelligence, and probability-based risk scoring into a unified decision-support pipeline. Logistic Regression is adopted as the core predictive model due to its robustness, interpretability, and model parsimony under highly imbalanced transaction data. Experimental results on accounting dataset consisting of 1,000 transaction records show that Logistic Regression achieved the highest PR-AUC of 0.9737 and ROC-AUC of 0.6458 compared with Random Forest and XGBoost. The risk scoring mechanism also ranked problematic transactions within the highest-risk group, supporting audit prioritization. In addition, graphical SHAP analysis provides qualitative insights by identifying Operating Expenses, log_Operating Expenses, transaction timing, Transaction Volume, Profit Margin, Revenue, Expenditure, Cash Flow, Gross Profit, and Accuracy Score as influential factors affecting transaction outcomes. These findings show that the proposed framework not only predicts transaction outcomes but also explains the accounting factors behind each decision. Overall, this study transforms conventional transaction classification into an interpretable, risk-oriented, and audit-driven intelligent accounting system for transparent financial decision support.
J. K. Siregar, Astari Dianty, Antonius Bimo Rentor et al.· International Seminar on Int...· 0 citations
It is concluded that each component of a layered fraud system contributes only under specific conditions, and that a plausible rationale from an investigation agent is not evidence of a better decision.
Artificial Intelligence (AI)-enabled Decision Support Systems (DSS) have become fundamental components of modern enterprise ecosystems, facilitating intelligent automation across customer engagement, financial risk management, and network operations. Despite remarkable advances in deep learning and Large Language Models (LLMs), the opaque nature of these models presents significant challenges in terms of explainability, trustworthiness, accountability, and regulatory compliance, limiting their adoption in mission-critical decision-making environments. To address these limitations, this paper proposes an Explainable Multi-Agent Artificial Intelligence Framework for Decision Intelligence (XMAI-DI), a unified cross-domain architecture that integrates collaborative intelligent agents, retrieval-augmented enterprise knowledge, and explainable AI techniques to generate transparent, reliable, and auditable decision outcomes. The proposed framework employs specialized autonomous agents dedicated to customer recommendation and inventory optimization, fraud detection and financial risk assessment, and network anomaly detection and intelligent traffic management. A Retrieval-Augmented Generation (RAG) module enriches agent reasoning by dynamically incorporating enterprise knowledge repositories, while an Explainability Orchestration Layer combines SHAP-based global feature attribution, LIME-based local explanations, causal inference, and governance-aware auditing to provide comprehensive and human-interpretable decision justifications. Furthermore, an adaptive orchestration mechanism coordinates inter-agent communication, confidence estimation, and knowledge refinement to improve decision consistency and operational scalability across heterogeneous enterprise environments. Experimental evaluation across representative retail, financial, and networking scenarios demonstrates that the proposed framework significantly improves decision transparency, interpretability, operational efficiency, and governance compliance while maintaining competitive predictive performance. The integration of explainable reasoning, collaborative multi-agent intelligence, and enterprise knowledge retrieval establishes a scalable foundation for trustworthy next-generation Decision Support Systems capable of supporting responsible AI deployment in complex cross-domain enterprise applications.
Abhignan Srivatsava Sribhashyam, Nivedan Suresh, Chaitanya Tumma, Supraja Ayyamgari· International Journal of Adv...· 0 citations
The study introduces the Stability–Transparency–Reliability (STR) model, which advances TPB, Socio-Technical Systems Theory, and the Dynamic Capabilities View by reframing XAI from a static interpretability output into a recursive governance capability, formalised through the concept of Interpretative Agility, with direct implications for financial institutions operating under the EU AI Act.