Aug 2026· Journal of Intelligent Decision Making and Information Science· Vol 3, pp. 39-51· 0 citations· 20 references
TL;DR
Analysis indicates that ensemble models incorporating XGBoost with LSTM networks achieve accuracy exceeding 98% with substantially reduced false positive rates, and critical challenges including model interpretability, data privacy, and deployment scalability are examined.
Abstract
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.
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
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
Financial fraud detection remains a critical challenge in digital ecosystems. This survey presents a comprehensive review of artificial intelligence (AI)-driven fraud detection techniques from 2015 to 2025, analyzing over 60 key studies. The evolution is traced across three phases: Early stage (classical machine learning (ML)), rule-based systems, data imbalance), algorithmic growth (deep learning (DL)), temporal analysis, hybrid models), and smart innovation (graph neural networks (GNNs)), federated learning (FL), explainable AI (XAI)). Major challenges include severe data imbalance, dynamic fraud patterns, black-box interpretability, privacy preservation, and adversarial robustness. Emerging techniques such as GNNs, FL, and reinforcement learning (RL) have shown superior performance in detecting organized and real-time fraud. Six future directions are proposed: Multimodal data fusion, continual learning (CL), large language models (LLMs) for textual anomaly detection, blockchain integration, human-in-the-loop explainable systems, and standardized evaluation benchmarks. This review provides a structured framework for developing intelligent, secure, and trustworthy fraud detection systems in financial institutions. It serves as a roadmap for researchers and practitioners aiming to address evolving fraud threats in cloud-based, distributed, and privacy-sensitive environments.
Received: 14 November 2025 | Revised: 9 February 2026 | Accepted: 7 June 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
The data that support the findings of this study are openly available in the UCI Machine Learning Repository at https://archive.ics.uci.edu/.
Author Contribution Statement
Elham Shamsinejad: Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing. Hamid Banirostam: Conceptualization, Methodology, Validation, Writing - review & editing, Supervision, Project administration.
E. Shamsinejad, Hamid Banirostam· FinTech and Sustainable Inno...· 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
The rapid digitalization of financial services has transformed the global financial ecosystem, enabling faster transactions, enhanced customer experiences, and greater financial inclusion. However, this digital transformation has simultaneously increased the complexity, scale, and sophistication of financial fraud. Traditional rule-based fraud detection systems often struggle to identify evolving fraud patterns, resulting in delayed responses, increased false positives, and substantial financial losses. Artificial Intelligence (AI)-powered real-time fraud monitoring systems have emerged as a transformative solution capable of detecting suspicious activities instantly through advanced data analytics, machine learning, deep learning, natural language processing, and behavioral intelligence. These systems continuously analyze vast volumes of transactional and non-transactional data, enabling financial institutions to identify anomalies, predict fraudulent behavior, and automate risk management processes with unprecedented accuracy and speed. This literature review examines the evolution, applications, technological foundations, benefits, challenges, and future directions of AI-powered real-time fraud monitoring systems in modern financial services. The review highlights how AI enhances fraud detection capabilities across banking, payment systems, insurance, digital wallets, cryptocurrencies, and investment platforms while discussing critical concerns related to privacy, algorithmic bias, explainability, cybersecurity, and regulatory compliance. The findings demonstrate that AI-driven fraud monitoring represents a fundamental component of modern financial security infrastructure and will continue to shape the future of fraud prevention in increasingly digital financial environments.
G. Onyarin· International Journal For Mu...· 0 citations
