Aug 2026· Magna Scientia Advanced Research and Reviews· 0 citations
TL;DR
It is concluded that credit unions and community banks can benefit from machine learning only when innovation is balanced with fairness, privacy protection, human oversight, vendor accountability, and community-centered governance.
Abstract
This paper explores data ethics and privacy issues in machine learning-driven financial systems, with particular attention to their implications for U.S. credit unions and community banks. As financial institutions increasingly adopt machine learning for credit risk assessment, fraud detection, anti-money laundering monitoring, personalization, and operational risk management, ethical concerns surrounding algorithmic bias, data misuse, transparency, explainability, and regulatory accountability have become more significant. Drawing on recent literature, the paper shows that although machine learning can improve efficiency and predictive accuracy, it may also reinforce historical inequities, expose sensitive customer data, and weaken trust when governance mechanisms are inadequate. The analysis emphasizes that smaller financial institutions face distinctive challenges because they often operate with limited technical resources, smaller datasets, and greater dependence on third-party vendors. It further discusses privacy-preserving approaches such as federated learning, explainable AI, data minimization, and ethical auditing as practical tools for responsible adoption. The paper concludes that credit unions and community banks can benefit from machine learning only when innovation is balanced with fairness, privacy protection, human oversight, vendor accountability, and community-centered governance.
This article examines how artificial intelligence and big data can support decision-making in private school management while preserving educational judgment, stakeholder trust, and institutional responsibility. Private schools operate under distinctive pressures, including enrolment competition, parental choice, financial sustainability, reputational risk, and regulatory compliance. These conditions make data-driven tools attractive for admissions forecasting, learning quality monitoring, student support, teacher development, financial planning, family communication, reputation analysis, and risk management. However, the use of AI in private schools also raises significant concerns, including data privacy, algorithmic bias, excessive surveillance, metric drift, vendor dependence, and the possible substitution of professional judgment with automated recommendations. Drawing on literature from AI in education, learning analytics, privatization, and data ethics, this article argues that AI should be understood as a socio-technical system rather than a neutral technical upgrade. It proposes a human-centered governance framework based on educational purpose, proportionality, contestability, transparency, stakeholder participation, vendor accountability, professional development, and continuous audit. The article concludes that AI and big data can improve private school decision-making only when they are embedded in responsible governance practices that strengthen, rather than weaken, educational values and institutional legitimacy.
This study examines data governance, bias mitigation, and legal risk within the context of a holistic Artificial Intelligence (AI) compliance framework for US companies operating in high-stakes sectors such as healthcare, finance, insurance, and critical infrastructure. The rapid adoption of AI systems has improved efficiency and decision-making capabilities. However, it has also introduced significant challenges related to algorithmic bias, lack of transparency, weak data governance, and increasing legal and regulatory exposure. Drawing on existing literature, the study highlights that inadequate governance structures and poor-quality datasets contribute to discriminatory outcomes, reduced accountability, and heightened compliance risks under evolving regulatory regimes. It further shows that algorithmic bias persists due to historical data inequalities and opaque machine-learning models, while legal frameworks such as privacy and anti-discrimination laws place additional obligations on organizations deploying AI systems. The study concludes 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
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
Temitope Onibaniyi, Umar Lawal· Texila international journal...· 0 citations
Financial fraud remains a persistent problem in the increasingly complex digital economy,
requiring a paradigm shift beyond traditional forensic accounting. The study integrates
behavioural analytics - leveraging natural language processing (NLP), machine learning, and
sentiment analysis - to unravel the human drivers behind fraud in three landmark Enron,
Wirecard, and FTX cases. The results show that behavioural indicators (e.g. evasive
communication, CEO overconfidence) precede financial irregularities by 6 to 24 months, and
machine learning models are 93 percent accurate in detecting fraud. In theory, we extend
Cressey's fraud triangle to the behavioral-financial feedback loop, emphasizing how
psychological rationalization and organizational culture interact with financial irregularities. In
practice, the study shows the potential of AI to improve risk scoring, while also highlighting ethical
trade-offs, such as the challenges of GDPR compliance in employee monitoring. Despite
limitations, including survivorship bias and data availability limitations, the findings support a
human-centric dashboard that integrates behavioral and financial metrics. Future research
should give priority to longitudinal studies of artificial intelligence tools in real-world
environments and cross-cultural analyses of fraud mitigation. This work provides a scalable
framework for preventing fraud through cross-sectoral innovation, reconciling technological
progress with ethical governance.
Samuel F. Johnson-Rokosu· Journal of Accounting and Fi...· 0 citations
Credit risk management (CRM) is a fundamental pillar of financial systems, attracting attention from practitioners and researchers. Traditional credit assessment methods have limitations in today's complex, fast-changing financial environment. Advances in machine learning (ML) and behavioral data analytics offer new possibilities for improving CRM through better models and performance. This paper provides a systematic review of ML applications in credit default prediction and early warning systems, critically synthesizing recent literature. It discusses three major dimensions: the evolution of ensemble learning algorithms, the use and issues of behavioral data in feature engineering, and advances in model explainability (XAI). The paper shows that ensemble learning models have superior predictive power and argues that behavioral data complement traditional datasets for underbanked populations, such as "credit invisibles." It makes a strong case for XAI being essential for model transparency, combating bias, and meeting regulatory requirements. The review also addresses class imbalance, data privacy, and ethical issues, with mitigation strategies. The review offers theoretical guidance and practical implications for financial institutions to improve risk control and build reliable early warning systems, outlining directions for future research.
Bojun Chen· Advances in Economics, Manag...· 0 citations
The study concludes that strengthening fraud detection and financial reporting integrity requires integrating analytics and internal controls within a unified governance framework supported by continuous monitoring, institutional accountability, and transparent oversight mechanisms.
Francesca Nyarkoa Kobla, Jessica Fosua Agyei· Magna Scientia Advanced Biol...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
