Aug 2026· International Journal of innovative inventions in Social Science and Humanities· Vol 03· 0 citations
TL;DR
It is argued 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.
Abstract
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.
The review highlights the transformative potential of AI while emphasizing the need for ethical, transparent, and secure implementation strategies to maximize effectiveness in combating increasingly sophisticated financial fraud schemes.
G. Onyarin· International journal of res...· 0 citations
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 is developed.
Prof. Roopa U Prof. Roopa U, Shrushti S Nelogi Shrushti S Nelogi· International Scientific Jou...· 0 citations
Given the rapid commoditization of generative artificial intelligence and high-velocity digital
transactions, the current rules-based models for detecting fraud have become obsolete. Scientific
writings which already exists focuses on highly focused and uni-modal approaches such as graph neural
networks for detecting camouflage in specific contexts or hybrid models for clustering data in tabular
form. These models, though mathematically sound, lack the required multimodal capabilities and lowlatency response times required for execution within digital transaction environments. To overcome these
encounters, a new four-pillar architecture is proposed, integrating generative artificial intelligence
defense mechanisms, machine learning models, threat intelligence systems, and alert management
systems. The research investigates whether integrating various open-source machine learning models
will outperform existing academic models for detecting fraud in transaction environments. The
methodology involves a comparative analysis of the existing academic models and the proposed
architecture in terms of processing latency, explainability, and the extent of the protection offered.
Results indicate that while academic models effectively detect fraud rings in digital transactions offline,
the proposed multimodal model achieves higher throughput and protects the authentication perimeter
of the transaction system from synthetic media and prompt injection attacks. Through this research, a
critical gap in the literature between academic models and real-world implementations is bridged. This
paper provides a comprehensive structure for enterprise systems to develop multimodal, explainable
machine learning models for fraud detection in high-velocity digital transactions.
David Dimitriu· Business Administration Stud...· 0 citations
The rapid growth of digital banking, e-commerce, electronic payments, and financial technology has increased both the volume and complexity of financial transactions, leading to greater fraud risks. Traditional rule-based fraud detection systems struggle to identify evolving and sophisticated fraud patterns. Artificial Intelligence (AI) offers an effective solution through machine learning, pattern recognition, and predictive analytics. This study explores AI-based fraud prevention systems in financial services, focusing on data acquisition, preprocessing, feature engineering, classification, anomaly detection, and real-time monitoring. Various machine learning models, including Random Forest, Support Vector Machines, Artificial Neural Networks, and Deep Learning, are evaluated for fraud detection. The findings indicate that AI-driven systems significantly improve fraud detection accuracy, reduce false positives, minimize operational losses, and enhance customer trust. The study concludes that AI is a critical component of modern financial security infrastructure and will play an increasingly important role in combating financial fraud.
E. Harris· International Journal of Com...· 0 citations
Digital payment platforms now process trillions of transactions annually, and the volume of associated fraud has grown in step with this expansion, with global card-fraud losses estimated in the tens of billions of dollars per year. Rule-based fraud controls, once the industry standard, are increasingly unable to keep pace with adaptive fraudsters and the scale of modern transaction streams. This paper presents a systematic literature review of machine learning (ML) approaches to fraud detection in digital payment systems. Drawing on peer-reviewed studies published between 2016 and 2025, the review organises the field into four broad technique families - classical supervised learning, deep and sequence-based learning, unsupervised and hybrid anomaly detection, and ensemble/reinforcement-learning architectures - and examines the datasets, evaluation metrics, and class-imbalance strategies that recur across the literature. The review finds that while deep sequential models and hybrid ensembles report the strongest detection accuracy on benchmark datasets, most published work still relies on a small number of public, heavily anonymised datasets, limiting generalisability to live payment environments. The paper concludes by proposing a conceptual, layered fraud-detection framework that synthesises the strengths identified in the literature and by outlining open research challenges, including concept drift, explainability, adversarial robustness, and privacy-preserving cross-institutional learning.
Keywords: fraud detection, digital payments, machine learning, deep learning, anomaly detection, credit card fraud, fintech security
Dean Margaret, Ajmal Haq A, Arun M S et al.· International Scientific Jou...· 0 citations
Insurance fraud is a big problem that the insurance industry is trying to solve. Economic loss, operational costs, and a decline in consumer trust are all consequences of insurance fraud. Traditional techniques of fraud detection, such as rule-based systems and human processes, frequently fail to detect more sophisticated fraud schemes. By analyzing complicated data in real-time, artificial intelligence (AI) has been shown to be an effective tool for automating the identification of fraud. Machine learning, explainable AI, federated learning, deep learning, reinforcement learning, natural language processing, and the most current approaches to AI methods in insurance fraud detection are included in this review. The paper also contains a discussion on traditional fraud detection methods, most popular insurance frauds, data preprocessing methods and some common metrics. Additionally, it looks at the latest research studies and points out the advantages and disadvantages of the available AI-driven approaches. Data quality, class imbalance, privacy preservation, model interpretability, scalability and regulatory compliance are among the key challenges that are critically analyzed.
Amit Jain· International Journal of Adv...· 0 citations