From Black Box to Boardroom: The Significance of Explainable AI (XAI) in Reducing Algorithmic Risk and Rebuilding Confidence in Digital Payment Systems
Jul 2026· Texila international journal of academic research· 0 citations
Abstract
The wide implementation of advanced Machine Learning (ML) models in digital payment systems, especially for fraud detection and credit risk assessment, has substantially improved operational efficiency and transaction security. The inherent opacity, often referred to as the black box character, of these high-performing algorithms poses considerable and mounting issues related to algorithmic fairness, stakeholder trust, and compliance with regulations. This article analyzes the growing strategic significance of Explainable Artificial Intelligence (XAI) as an important governance tool for mitigating algorithmic risk in financial services. The paper exposes how XAI, informed by Agency Theory and Institutional Theory, is not just a technical requirement but an essential institutional mechanism for ensuring regulatory accountability within frameworks like the EU AI Act, restoring public trust and identifying and alleviating systemic algorithmic bias in credit scoring and fraud risk assessment. A conceptual framework is introduced and it illustrates how XAI; using post-hoc interpretation methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations)- bridges the knowledge disparity between intricate AI models and various human stakeholders, including customers, fraud analysts, and regulators. This transformation shifts AI from a hypothetical institutional liability to a responsible, auditable, and governable asset within the digital payment ecosystem. The report concluded by describing key areas for forthcoming empirical research on the organizational problems associated with XAI implementation across various regulatory jurisdictions
The integrity and accuracy of financial data are prerequisites for market efficiency; however, data anomalies and quality issues severely compromise their “fitness for use” in sophisticated decision-making processes, such as value investing strategies. This article reviews the application of advanced artificial intelligence (AI) methods to enhance quality assurance, anomaly detection, and imputation within high-dimensional financial data streams. The paper critically evaluates both statistical-machine learning hybrids (e.g., ARIMA-LSTM) and deep learning combinations (e.g., autoencoder-based GANs), alongside Explainable Artificial Intelligence (XAI) techniques, assessing their utility against the strict auditability requirements of public trust institutions. The synthesized literature suggests that hybrid frameworks can potentially outperform monolithic approaches in detecting nonlinear manipulations and creating “high-fidelity” datasets. Furthermore, the study addresses the “black box” opacity challenge—a major barrier for regulatory and statistical agencies—discussing how methods like SHAP and LIME support, rather than independently ensure, the necessary interpretability of algorithmic decisions. Conclusions indicate that the synergy between the predictive power of advanced AI models and the transparency supported by XAI is a highly valuable component for modern market supervision, enabling effective data validation while supporting institutional accountability.
Krzysztof Podgórski· Journal of Official Statisti...· 0 citations
Financial supervisors increasingly face artificial intelligence (AI) systems whose internal logic they cannot directly inspect, forcing prudential and conduct regulators to rely on the disclosures, model documentation, and self-attested fairness testing of the firms they oversee. This dependency creates an information asymmetry that undermines effective oversight of credit scoring, anti-money laundering (AML), fraud detection, and insurance-underwriting models. This paper proposes a Supervisory Explainable AI (XAI) Toolkit, a privacy-preserving auditing platform inspired by the Bank for International Settlements (BIS) Innovation Hub's Project Noor, that equips regulators to independently probe and assess proprietary AI models without requiring firms to surrender raw data, model weights, or trade secrets. The toolkit combines a privacy-preserving query broker built on differential privacy, secure multi-party computation, and trusted execution environments with a model-agnostic explainability engine and a fairness-and-robustness metric compiler, translating opaque model logic into standardized, comparable supervisory metrics. We present the system architecture, a five-stage audit workflow, and an illustrative evaluation that quantifies the divergence between firm-reported and independently audited fairness scores across four financial use cases, together with the trade-off between privacy budget and audit fidelity. The results suggest that self-disclosed fairness metrics can materially overstate model fairness and that a modest privacy budget is sufficient to recover most of the audit signal needed for supervisory decision-making. We discuss governance, legal, and technical implications for deploying such toolkits within existing supervisory technology (SupTech) programs and outline directions for standardization and cross-border regulatory cooperation.
Andrew Moore, Samuel Allen· World Journal of Advanced Re...· 0 citations
The rapid adoption of digital technologies has significantly transformed the way banks and financial institutions evaluate loan applications. Machine learning (ML) models are widely used in credit risk assessment to analyze large volumes of financial data and support faster and more reliable lending decisions. However, many of these models operate as black-box systems that provide limited explanation for loan approval or rejection outcomes. In financial environments, where decisions directly impact borrowers and institutional risk exposure, lack of transparency may reduce trust and raise concerns regarding fairness and accountability. To address these challenges, this study proposes a Transparent and Explainable Artificial Intelligence (XAI) framework for risk-aware loan approval decision support. The proposed framework integrates predictive modeling with explainability techniques such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Counterfactual Explanations, and Permutation Feature Importance. These techniques provide both global insights into model behavior and clear explanations for individual loan decisions. In addition, fairness evaluation mechanisms are incorporated to detect potential bias across sensitive attributes. Experimental results demonstrate that integrating explainability improves transparency and user confidence while maintaining strong predictive performance, thereby supporting reliable and responsible AI-based loan approval systems for financial institutions.
Ch.Padma, N. Bhavani, G. B. Prakash et al.· 2026 4th International Confe...· 0 citations
Artificial intelligence (AI)-driven fraud detection systems in FinTech ecosystems increasingly face a governance tension between high predictive accuracy and limited regulatory transparency, a gap that existing reviews have not addressed through an integrated behavioural, technical, and institutional lens. This study synthesises 99 Scopus-indexed, ABDC-ranked journal articles (2015–2026) using PRISMA 2020 and the SPAR-4-SLR protocol, integrating the Theory of Planned Behaviour (TPB) within an Antecedents–Decisions–Outcomes (ADO) framework to examine organisational adoption of explainable AI (XAI) in financial fraud detection. Three antecedent clusters are identified: attitudinal (algorithmic complexity, model opacity, data imbalance), normative (regulatory compliance, ethical expectations), and control-based (technical self-efficacy, organisational readiness)—which drive decision mechanisms including post hoc interpretability tools (SHapley Additive exPlanations [SHAP], Local Interpretable Model-Agnostic Explanations [LIME]), ethical governance protocols, and human-in-the-loop oversight. These produce outcomes across precision (reduced false positives, improved decision accuracy), compliance (audit transparency, institutional legitimacy), and cognitive (user acceptance, procedural justice) dimensions. 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.
SHAP and LIME are now standard tools for interpreting black-box predictions, yet their outputs can vary substantially when the input is perturbed by small amounts of noise--a problem we observed firsthand in our previous work on food security in Madagascar (Ralinirina et al., 2025). This variability raises the question of whether such explanations can be trusted at all. We address it by constructing an auditing protocol that measures two properties of any post-hoc explainer: robustness (how stable the explanation is under input perturbation) and fidelity (whether the features deemed important actually drive the model's prediction). These two quantities are combined into a single Trust Score. We run the protocol on a multi-sectoral dataset from Madagascar (83 features, 253 records, 4 malnutrition classes) using three classifiers and two explainers, plus their regularized counterparts. The results are sobering: models with AUC above 0.99 can produce numerically degenerate or flatly uninformative explanations, and fidelity scores lose discriminative power when the model is overfitted. These findings suggest that auditing XAI outputs is not optional but necessary, particularly when they inform decisions in sensitive domains.
Rosa Elysabeth Ralinirina, J. Ralaivao, Niaiko Michaël Ralaivao et al.· 0 citations
It is concluded that a holistic AI compliance framework integrating data governance, bias mitigation strategies, legal oversight, cybersecurity, and ethical accountability is essential for ensuring responsible and trustworthy AI deployment in high-stakes environments.
Joy Oluchi Nwachukwu, Thaddaeuse Odhiambo, Dorcas Akorkor Apaflo et al.· Journal of Economic, Finance...· 0 citations