Aug 2026· Journal of Accounting and Financial Management· 0 citations
TL;DR
This work provides a scalable framework for preventing fraud through cross-sectoral innovation, reconciling technological progress with ethical governance through cross-sectoral innovation and cross-cultural analyses of fraud mitigation.
Abstract
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.
This research paper examines the role of artificial intelligence (AI) in detecting and preventing financial fraud, with particular attention to the analytics techniques that underpin modern fraud management systems. As financial transactions increasingly migrate to digital channels, the volume, velocity, and variety of transactional data have outpaced the capabilities of traditional rule-based fraud detection systems, creating an urgent need for adaptive, data-driven approaches. Drawing on theoretical frameworks including the Fraud Triangle Theory, Statistical Learning Theory, and the Technology Acceptance Model, this paper develops a conceptual model demonstrating how AI-enabled analytics techniques -- encompassing supervised machine learning, unsupervised anomaly detection, deep learning, and graph-based network analytics -- directly and indirectly enhance fraud detection accuracy, response speed, and organisational risk posture.
The paper reviews relevant literature, analyses real-world case studies from payment networks, commercial banks, and fintech platforms, and proposes a comprehensive framework linking analytics capability to fraud detection outcomes. Findings suggest that AI-driven fraud analytics not only improves detection accuracy and reduces false positives relative to legacy rule-based systems, but also enables real-time intervention that limits financial losses and preserves customer trust. The paper also identifies key challenges to implementing AI-based fraud analytics at scale, including class imbalance in fraud datasets, adversarial adaptation by fraudsters, model explainability requirements, and data privacy constraints. Future directions, including generative AI for fraud narrative analysis, federated learning for privacy-preserving analytics, and graph neural networks for real-time network analysis, are discussed. This work contributes to the growing literature on financial analytics, risk management, and applied artificial intelligence, and holds practical implications for analytics teams, risk officers, and financial regulators.
Keywords: Artificial Intelligence, Financial Fraud, Fraud Detection, Machine Learning, Data Analytics, Predictive Analytics, Anomaly Detection, Risk Management, Banking, Explainable AI
Prof. Roopa U Prof. Roopa U, Shrushti S Nelogi Shrushti S Nelogi· International Scientific Jou...· 0 citations
For most of the twentieth century, the detection of financial fraud rested on an uncomfortable compromise: because no auditor or investigator could examine every transaction, assurance was built on samples, and fraud that fell outside the sample escaped notice. Artificial intelligence promises to dissolve that compromise by subjecting entire populations of transactions, disclosures, and communications to continuous algorithmic scrutiny. This article evaluates how far that promise has been kept. Drawing on three decades of empirical research in accounting, information systems, and computer science, it examines the performance of supervised classifiers, anomaly detection, natural language processing, and network analytics against complex schemes such as financial statement manipulation, collusive procurement fraud, and layered transaction fraud. The evidence supports a qualified conclusion. Machine learning models now outperform traditional ratio-based screens by meaningful margins, yet their effectiveness is constrained by severe class imbalance, biased training labels drawn only from detected fraud, adversarial adaptation by offenders, and opacity that sits awkwardly with evidentiary standards in criminal and regulatory proceedings. The article argues that artificial intelligence is best understood as an instrument of triage rather than adjudication, and it draws out the governance, forensic, and pedagogical consequences of that position for both mature and emerging markets, including African jurisdictions such as Ghana.
Dr. Gaduga Godwin, Esq· International Journal of inn...· 0 citations
Financial fraud has aggressively transformed along with the digital era, this change being largely driven by the growing reach of internet banking and mobile payments as well as cyber threats, which have been getting more advanced and which exploit not only technical weaknesses but also people's behavior. Old-fashioned detection systems that run on rules still do have their merits; however, they mostly cannot cope with the amount and intricacy of fraud patterns that we see nowadays. That is why we have seen the rise of artificial intelligence (AI), which is a very potent means of spotting irregularities, understanding how people behave, and facilitating instant decisions in fraud detection. On the other hand, Customer Relationship Management (CRM) systems have become indispensable to financial environments through the gathering of customer information, records of contacts, and insights on behavior. This paper looks at how these two fields overlap and points out ways in which the blending of AI-based analytics in CRM systems can lead to a remarkable rise in proactive fraud prevention. Using customer-focused data, such as their record of purchases, modes of communication, and levels of engagement, AI programs are capable of spotting very subtle changes that could signal an attempt at fraud and at the same time, they are able to significantly reduce the number of false alarms. The technique suggested here describes a system for the detection of fraud, which is both dynamic and adaptable and which is obtained through the synthesis of machine learning techniques and the CRM data streams. Through a case-based study, it is shown how this unified method can lead to an increase in detection performance, shortening of the time for the response, and higher customer confidence in comparison to the existing systems.
Satyendra Kumar Vanapalli· International Journal of Mac...· 0 citations
As financial fraud becomes more sophisticated and financial services are increasingly digitized, artificial intelligence (AI) and machine learning are emerging as pivotal technologies for risk management and compliance. While research into AI-driven fraud detection is advancing rapidly, the intellectual structure and theoretical underpinnings remain fragmented. This paper provides a systematic review of 118 peer-reviewed articles published between 2015 and 2025, combining bibliometric science mapping with the SPAR-4-SLR protocol to ensure rigour, transparency and replicability. Through co-word network analysis, thematic mapping and conceptual clustering, the study traces the field’s evolution from rule-based systems to adaptive anomaly detection, explainable AI and compliance models, with a focus on digital payment ecosystems and blockchain-enabled applications. The analysis highlights key theoretical anchors, including Fraud Triangle Theory, Agency Theory, Game Theory, Trust and Signalling Theories and regulatory compliance perspectives. It also identifies underexplored areas such as federated learning, algorithmic auditing and cross-jurisdictional intelligence. By mapping theoretical foundations and thematic development, this study offers an evidence-based account of how AI in fraud detection has evolved. It concludes by proposing a future research agenda emphasizing transparency, ethical assurance and global governance alignment, advancing financial risk management through conceptual clarity, methodological guidance and actionable pathways for responsible AI adoption.
Devansh Gupta, Priyanka Chugh, Poonam Mahajan· South Asian Journal of Busin...· 0 citations
Banking fraud presents a persistent challenge in the digital era, with financial losses continuing to escalate as transaction volumes grow exponentially. This paper examines AI-enabled fraud detection frameworks that leverage big data analytics, machine learning, and deep learning methodologies. The review systematically categorizes existing approaches across supervised learning, anomaly detection, ensemble methods, and deep neural network architectures. Special attention is directed toward hybrid frameworks that combine multiple techniques to address extreme class imbalance, concept drift, and real-time processing constraints. Analysis indicates that ensemble models incorporating XGBoost with LSTM networks achieve accuracy exceeding 98% with substantially reduced false positive rates. Critical challenges including model interpretability, data privacy, and deployment scalability are examined, alongside promising directions for future research in this domain.
Kapil Sharma¹, Rupali Bhartiya², Dheeraj Tiwari³ et al.· Journal of Intelligent Decis...· 0 citations
Healthcare fraud is a major problem in the United States, which annually totals tens of billions of dollars and impacts financial sustainability, as well as patient confidence. Traditional forms of detection, such as manual risk audits and static category rule-based solutions, are becoming less effective in the face of fraudster sophistication and ever-evolving fraudulent tactics. Based on a systematic review of peer-reviewed articles, policy reports, and industry white papers, this article examines how artificial intelligence (AI) can be used to reduce financial fraud in U.S. healthcare systems. Results demonstrate the potential of machine learning, combined with NLP and predictive analytics, to add value in terms of detection accuracy, false-positive rate reduction, and near real-time fraud prevention. Moreover, integration with AI human-in-the-loop is demonstrated to increase efficiency with retention of supervision. However, some obstacles persist around data quality, bias of algorithms, interpretability, and regulatory aspects. In all, AI has strong potential as a game-changer in the fight against healthcare fraud if it is rolled out subject to robust governance and ethical considerations.
Afari Ntiakoh, Isaiah Thompson Ocansey, Christian Amoakoh· Magna Scientia Advanced Rese...· 0 citations