Aug 2026· International Journal of Research Publication and Reviews· Vol 7, pp. 1358-1369· 0 citations
TL;DR
This study engineers an Explainable Artificial Intelligence Risk-Based Internal Auditing Framework integrating multi-source banking data, dynamic risk scoring, anomaly detection, control-risk mapping, explainability mechanisms, and human-in-the-loop validation.
Abstract
Digitally transformed banking infrastructures increasingly operate through interconnected cloud platforms, APIs, algorithmic decision systems, real-time payment networks, and automated controls, fundamentally altering how operational, cyber, credit, compliance, and financial-crime risks emerge and propagate. These environments generate continuous volumes of heterogeneous data that exceed the capacity of periodic, rules-based internal auditing, accelerating adoption of artificial intelligence for risk detection and audit prioritisation. However, predictive accuracy alone is insufficient for assurance: auditors must establish why a model identifies an activity as high-risk, which variables influence its judgement, whether outputs remain reproducible, and how resulting evidence supports defensible audit conclusions. This study engineers an Explainable Artificial Intelligence Risk-Based Internal Auditing Framework integrating multi-source banking data, dynamic risk scoring, anomaly detection, control-risk mapping, explainability mechanisms, and human-in-the-loop validation. The framework translates model predictions into traceable risk drivers and evidence-linked audit priorities while incorporating data lineage, model governance, regulatory requirements, and continuous monitoring. It establishes an auditable pathway from banking transactions and control signals through AI inference and explanation to risk prioritisation, auditor validation, and assurance decisions across heterogeneous global banking infrastructures.
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.
Public-funded institutions operate under increasing pressure to demonstrate accountability,
transparency, and measurable impact. Despite the widespread collection of programmatic and
financial data, many institutions lack formally engineered systems that ensure information used
in funding, compliance, and strategic decisions is validated, comparable, and decision ready.
This paper introduces the concept of Decision-Grade Intelligence (DGI) and presents a
preventive validation framework for institutions managing public or grant-administered
resources. The study distinguishes reactive audit correction from preventive validation
embedded within institutional data architecture. It proposes a structured systems model
integrating data integrity controls, governance checkpoints, comparability standards, and
decision-support calibration mechanisms prior to executive action. By applying engineering
principles of precision, reliability, and optimization to institutional analytics environments, the
framework seeks to reduce downstream accountability failures, improper allocations, and
corrective expenditures. The paper further proposes measurable indicators of institutional
validation maturity and outlines implementation pathways adaptable across nonprofit, publicsector–adjacent, and hybrid governance contexts. By reframing accountability as an
engineering systems challenge rather than a reporting function, this research contributes a
cross-sector model for strengthening public trust, fiscal stewardship, and long-term
institutional sustainability
Odinaka-olisa James Okonkwo· INTERNATIONAL JOURNAL OF SOC...· 0 citations
The rapid digitalization and interconnectivity of global supply chains have significantly increased exposure to cybersecurity risks, particularly through third-party dependencies, IoT integration, remote access, and shared digital infrastructures. Traditional supply chain auditing approaches, which rely heavily on periodic compliance checks and retrospective assessments, are increasingly insufficient for identifying dynamic and systemic cyber risks. This study proposes an integrated machine learning-based cybersecurity risk framework for supply chain auditing and examines how ML models capture multidimensional and time-related cybersecurity risk indicators. A quantitative experimental design was adopted using a hybrid dataset that combines empirically grounded cybersecurity indicators with simulated supply chain cyber risk scenarios. The synthetic data were generated through controlled attack scenarios using AttackIQ and Cymulate simulation platforms and were conceptually aligned with the NIST Cybersecurity Framework, ENISA guidelines, and the Verizon DBIR. IBM Watsonx was used to develop and evaluate supervised and time-series models, including Random Forest and ARIMA. The integrated risk flag was constructed as a composite cybersecurity risk representation derived from standardized audit-relevant indicators. The findings show that the integrated ML-based risk framework achieved strong classification performance, with accuracy = 98.5% and F1 > 0.98. The results primarily reflect the internal consistency of the constructed cybersecurity risk framework rather than direct prediction of real-world cyber incidents. Sensitivity analyses confirmed the robustness of the framework under different synthetic data conditions. Random Forest effectively captured complex nonlinear risk patterns, while ARIMA modeled temporally persistent risk indicators. In contrast, compliance metrics showed limited ability to reflect actual cybersecurity risk exposure.
Hossam Hassan, Rehab Hashem, Ahmad A. Abu-Musa· Future Business Journal· 0 citations
Artificial Intelligence (AI) has emerged as one of the most transformative technologies influencing organizational governance, financial oversight, and risk management practices worldwide. The integration of AI into internal audit functions has significantly altered the traditional audit landscape by enhancing operational efficiency, improving fraud detection capabilities, strengthening risk assessment procedures, and enabling real-time auditing practices. This research paper examines the transformative role of AI technologies such as machine learning, neural networks, natural language processing, robotic process automation, and predictive analytics in reshaping internal audit operations. The study explores how AI-driven systems automate repetitive audit tasks, analyze large volumes of structured and unstructured data, and improve audit accuracy while reducing operational costs. Furthermore, the paper evaluates the challenges associated with AI adoption, including ethical concerns, cybersecurity risks, data privacy issues, technological dependence, and skill gaps among auditors. A comparative analysis between traditional and AI-enabled audit practices is also presented to assess the effectiveness and efficiency of AI-based auditing systems. The study concludes that AI is not replacing internal auditors but transforming their roles into more strategic, analytical, and advisory-oriented functions. Organizations that successfully integrate AI into their audit frameworks can achieve greater transparency, stronger governance, and improved organizational resilience in an increasingly digital business environment.
F. Raidah, M. Jobair, Md. Halimuzzaman et al.· American Journal of Financia...· 0 citations
Artificial intelligence increasingly shapes consequential decisions, yet predictive accuracy alone is insufficient when users cannot understand, contest, or safely act on model outputs. This systematic review examines how explainable artificial intelligence (XAI) is combined with predictive analytics across healthcare, finance, cybersecurity, and sustainable systems. Following a PRISMA-informed protocol, 35 peer-reviewed studies published between 2018 and 2025 were selected from multidisciplinary databases and coded for application, model class, explanation technique, validation strategy, stakeholder, and implementation maturity. The synthesis shows that SHapley Additive exPlanations, feature importance, Local Interpretable Model-agnostic Explanations, saliency methods, and rule-based surrogates dominate current practice. Healthcare studies emphasize diagnostic reasoning, risk stratification, antimicrobial resistance, medical imaging, and supply-chain resilience; finance studies prioritize credit assessment, fraud detection, information security, and technology adoption; cybersecurity studies focus on intrusion detection, malware analysis, and analyst-centered threat interpretation; sustainable-systems research applies XAI to renewable-energy forecasting, load prediction, maintenance, and carbon-reduction planning. Across domains, explanations are most useful when they are stakeholder-specific, clinically or operationally plausible, stable under perturbation, and linked to an actionable decision. However, many studies validate predictive performance more thoroughly than explanation quality, rely on post-hoc tools without testing fidelity, and provide limited evidence from real-world users. The review proposes a cross-domain governance framework integrating model selection, explanation design, human evaluation, fairness testing, drift monitoring, and documentation. XAI should therefore be treated not as a visualization added after modeling, but as a socio-technical control layer that connects predictive performance with accountability, safety, and sustainable adoption across complex, regulated, and resource-constrained operational environments.
Daria A Vasileva, Sophia M Reynolds· International Journal of Med...· 0 citations
— This study investigates how Artificial Intelligence (AI) can transform auditing by improving fraud detection and audit quality. However, traditional methods, limited by sampling and manual inspection, failed to detect complex frauds such as Luckin Coffee. Using the Technology – Organization – Environment (TOE) framework and Socio-Technical Systems (STS) theory, the research adopted a qualitative case study design. Audit failures were reconstructed through triangulated evidence and counterfactual reasoning, assessing how Machine Learning (ML) and Natural Language Processing (NLP) could have detected anomalies in real time. Findings show AI provides broader coverage, timeliness, and granularity while preserving professional skepticism. The study contributes by situating AI adoption within socio-technical and organizational contexts and proposing a framework for responsible implementation, and it is crucial to position AI as a tool that complements, rather than substitutes, human auditors.
Hong Yang· International Journal of Tra...· 0 citations