2026· International journal of research and innovation in social science· 0 citations
TL;DR
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.
Abstract
Financial fraud has emerged as one of the most significant threats to global economic stability, resulting in billions of dollars in annual losses for financial institutions, businesses, governments, and individual consumers. The rapid digitization of financial services, expansion of online banking, growth of electronic payment systems, and increasing sophistication of cybercriminal activities have rendered traditional fraud detection mechanisms insufficient. Artificial Intelligence (AI) has transformed financial fraud detection by enabling automated identification of suspicious transactions, real-time monitoring of financial activities, predictive analytics, and adaptive learning capabilities that continuously improve detection accuracy. This literature review examines the current applications of AI in financial fraud detection, including machine learning, deep learning, natural language processing, anomaly detection, and graph-based analytical approaches. The review further explores major challenges associated with AI deployment, such as data quality limitations, model interpretability, privacy concerns, adversarial attacks, regulatory compliance, and algorithmic bias. Additionally, emerging trends and future directions are discussed, including explainable AI, federated learning, blockchain integration, quantum-enhanced fraud analytics, and autonomous fraud prevention systems. 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.
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
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
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
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
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.
Dr. Gaduga Godwin, Esq· International Journal of inn...· 0 citations
Financial fraud has emerged as one of the most significant challenges in the modern digital economy due to the rapid growth of online banking, mobile payments, electronic commerce, and digital financial services. Traditional fraud detection systems primarily rely on predefined rules and expert-generated patterns; however, such approaches often fail to identify newly emerging fraud techniques and sophisticated fraudulent behaviors. Consequently, there is an increasing need for intelligent and adaptive fraud detection systems capable of learning from historical transaction data and identifying suspicious activities with high accuracy.
This research presents an intelligent machine learning-based approach for fraud detection in financial transactions. The study investigates and compares the effectiveness of multiple machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), in detecting fraudulent financial activities. The proposed methodology incorporates data preprocessing, feature engineering, handling class imbalance, model training, and comprehensive performance evaluation.
A publicly available credit card transaction dataset is utilized for experimental analysis. Data preprocessing techniques such as normalization, missing value handling, and feature selection are applied to improve model performance. Since fraud datasets are typically highly imbalanced, class balancing techniques are incorporated to enhance the detection of minority fraudulent transactions.
The performance of the selected algorithms is evaluated using Accuracy, Precision, Recall, F1-Score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC).
Comparative analysis demonstrates that ensemble learning methods, particularly Random Forest and XGBoost, outperform traditional classification techniques by achieving higher detection rates and lower false positive rates. Furthermore, the study highlights the practical applicability of machine learning models in real-world financial environments where rapid and accurate fraud detection is essential.
The findings of this research contribute to the growing field of financial fraud analytics by providing a comparative framework for evaluating machine learning algorithms under consistent experimental conditions. The proposed approach offers valuable insights for financial institutions, banking organizations, and cybersecurity professionals seeking to enhance fraud prevention systems and minimize financial losses.
Keywords: Fraud Detection, Machine Learning, Financial Transactions, Random Forest, XGBoost, Artificial Neural Networks, Classification, Financial Security.
Dr. Abdul Majid Farooqi Dr. Abdul Majid Farooqi, Laiba Khan LAIBA KHAN· International Scientific Jou...· 0 citations