Jul 2026· International Journal For Multidisciplinary Research· 0 citations· 54 references
TL;DR
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.
Abstract
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.
It is concluded 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
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
The rapid digitalisation of financial services has created enormous opportunities for banks, financial institutions and businesses, but it has also increased their exposure to fraud and financial risks. Conventional fraud detection systems, which mainly depend on predefined rules and manual investigation, are increasingly challenged by sophisticated and rapidly changing fraudulent activities. Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning, Natural Language Processing and anomaly detection, provides financial institutions with new ways to identify suspicious patterns, assess risks and respond to potential threats in real time. This paper examines the role of AI in fraud detection and financial risk management, with particular attention to its applications in banking and digital financial services. The study is conceptual and descriptive in nature and is based on primary data and secondary information obtained from academic literature and reports of financial and international institutions. The paper discusses how AI can improve transaction monitoring, credit risk assessment, anti-money-laundering activities, cybersecurity and predictive risk management. The study argues that AI should not be viewed as a complete replacement for human judgement. Rather, the most effective approach is likely to combine AI-driven analysis with human expertise, appropriate governance and continuous monitoring. The paper concludes that responsible and explainable AI can significantly strengthen the ability of financial institutions to prevent fraud and manage financial risks while maintaining customer trust and financial stability.
V. Soniya· International journal of res...· 0 citations
Financial institutions process high transaction volumes through digital payment channels, but conventional rule-based fraud detection systems often fail to adapt to changing fraud patterns and may produce excessive false alerts. This study developed and evaluated an intelligent real-time stacked ensemble prototype for credit card fraud detection in financial institutions. The system combined supervised fraud probability estimates from Random Forest and XGBoost with anomaly score evidence from Isolation Forest, and used Logistic Regression as a meta-classifier to generate the final transaction-level decision. The prototype was implemented in Python using Pandas, NumPy, scikit-learn, imbalanced-learn, XGBoost, Seaborn, Matplotlib, and Joblib. The Credit Card Fraud Detection dataset was used for experimentation, containing 284,807 transactions, 30 input features, and 492 fraudulent cases. The data were split using stratified 80:20 sampling; Time and Amount were standardised using training set statistics only, and SMOTE was applied only to the training partition to prevent test-set leakage. The held-out test set retained the original imbalanced distribution of 56,864 legitimate transactions and 98 fraudulent transactions. Random Forest achieved the highest precision and F1-score, with precision of 0.8454, recall of 0.8367, F1-score of 0.8410, false positive rate of 0.00026, and AUC-ROC of 0.9731. XGBoost achieved the highest base-model recall of 0.8673 and AUC-ROC of 0.9750, but produced 136 false positives. Isolation Forest achieved AUC-ROC of 0.8532 but failed to detect fraudulent transactions as a standalone binary classifier under the implemented configuration. The stacked ensemble achieved accuracy of 0.9993, precision of 0.7778, recall of 0.8571, F1-score of 0.8155, false positive rate of 0.00042, and AUC-ROC of 0.9758. The results show that the stacked ensemble produced a usable prototype-level fraud decision layer by combining supervised and anomaly based evidence, although threshold calibration, explainability, and validation on local institutional data remain necessary before operational deployment.
Nwadike U. S., Emmah V. T., M. D.· Journal of Artificial Intell...· 0 citations
Digital payment systems have become the backbone of global commerce, but their rapid expansion has been paralleled by a sharp rise in payment fraud, identity theft, and cyber-enabled financial crime. This paper examines the role of Artificial Intelligence (AI) in enhancing the security and fraud-detection capability of digital online payment systems, drawing on recent industry reports, regulatory data, and empirical machine learning studies. The study adopts a descriptive-analytical approach, synthesising secondary data from central bank publications, market-research reports, and peer-reviewed comparative studies of algorithms such as Random Forest, Artificial Neural Networks, Support Vector Machines, and Gradient Boosting Results show that AI-powered fraud detection tools, such as the ensemble method Random Forest, consistently outperform their human counterparts, with accuracy between 92 and 100 percent in experimental and production environments; real-time behavioural analytics, biometric authentication and natural language processing also take fraud protection beyond transaction-level screening to include phishing, social engineering and mule-account detection. The scale of the challenge, as well as the regulatory response to the menace of digital payment fraud, is evident from the Indian Unified Payments Interface (UPI) ecosystem, where the value of digital payment fraud fluctuated despite an over 40 per cent increase in the number of transactions year-on-year, and the Reserve Bank of India's (RBI) MuleHunter.AI initiative. However, the paper still points to certain issues that have not been overcome, such as class imbalance, manipulation by a growing swarm of AI-savvy fraudsters, a lack of explanation, data-privacy restrictions, and disparity in adoption by institutions of varying sizes. The paper concludes that AI plays an essential role in the current payment-security architecture, but it must be complemented with a multi-layered approach that includes technological solutions, regulations, and consumer-awareness initiatives to effectively withstand the ever-changing threat landscape.
Ch. Keerthi, B. Nandini· Advanced International Journ...· 0 citations
The suggested framework employs a stacked ensemble approach in which Logistic_Regression, Decision_Tree, and XGBoost act as base models, while a meta-classifier produces the final fraud prediction.
G. S. Rahul Gorpade, A. M· International Research Journ...· 0 citations
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