ARTIFICIAL INTELLIGENCE TECHNIQUES FOR FINANCIAL FRAUD DETECTION: A COMPREHENSIVE REVIEW OF MACHINE LEARNING, DEEP LEARNING, AND HYBRID MODELS

Farrukh H. Boltaev; Nazarbek G. Rakhmatullaev

doi:10.55439/EIT/vol13_iss2/707

Авторы

Farrukh H. Boltaev Independent Researcher, Tashkent, Uzbekistan
Nazarbek G. Rakhmatullaev Independent Researcher, Doha, Qatar

DOI:

https://doi.org/10.55439/EIT/vol13_iss2/707

Ключевые слова:

artificial intelligence, financial fraud detection, machine learning, deep learning, hybrid models, decision trees, random forest, neural networks, support vector machines, ensemble learning, data imbalance, explainable AI, federated learning, graph neural networks, privacy preservation, anomaly detection, predictive analytics, financial security.

Аннотация

In the contemporary financial landscape, the increasing sophistication of fraudulent schemes poses substantial challenges to traditional rule-based detection systems. As financial transactions become more digitized and complex, Artificial Intelligence (AI) has emerged as a transformative paradigm for enhancing the accuracy, adaptability, and efficiency of fraud detection mechanisms. This comprehensive review critically examines recent developments in AI-driven approaches for financial fraud detection, focusing on three methodological pillars: machine learning, deep learning, and hybrid models. The analysis explores core algorithms—including decision trees, random forests, support vector machines, neural networks, and ensemble learning techniques—emphasizing their methodological strengths, practical limitations, and performance implications. Furthermore, the paper identifies key challenges such as data imbalance, model interpretability, privacy preservation, and the dynamic evolution of fraudulent behaviors. Particular attention is devoted to emerging directions such as federated learning, graph neural networks, and explainable AI, which are expected to underpin the next generation of transparent, privacy-preserving, and globally adaptive financial fraud detection frameworks.

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