review · Journal Of Big Data
Fraud attacks on banking systems, financial institutions, and credit card holders are rising rapidly, creating strong demand for more capable detection systems. A review of recent research evaluates artificial intelligence, machine learning, deep learning, and meta-heuristic optimisation methods applied to credit card fraud detection. Comparing these approaches identifies their specific strengths and weaknesses, alongside persistent limitations in existing machine learning and deep learning models. Understanding these constraints is essential for designing more robust tools capable of flagging varied and evolving deceptive schemes. The core conclusion emphasises that detection systems require ongoing development to remain responsive to emerging fraudulent practices.
Credit card fraud threatens the security of financial institutions and causes significant losses for everyday consumers. Understanding the performance and limits of current artificial intelligence methods allows developers and banks to build more reliable defences. Continuous improvement of these models ensures that security measures keep pace with increasingly sophisticated and changing fraudulent schemes.
Financial institutions, fintech firms, and banking software vendors could use insights from this review to benchmark and improve automated fraud detection tools. Because the work is a review of existing academic literature rather than a newly deployed algorithm or product, it sits at an early, analytical stage. Practical application depends on software developers adopting recommended model combinations to address known machine learning shortcomings in operational transaction pipelines.
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Abstract The rapid increase of fraud attacks on banking systems, financial institutions, and even credit card holders demonstrate the high demand for enhanced fraud detection (FD) systems for these attacks. This paper provides a systematic review of enhanced techniques using Artificial Intelligence (AI), machine learning (ML), deep learning (DL), and meta-heuristic optimization (MHO) algorithms for credit card fraud detection (CCFD). Carefully selected recent research papers have been investigated to examine the effectiveness of these AI-integrated approaches in recognizing a wide range of fraud attacks. These AI techniques were evaluated and compared to discover the advantages and disadvantages of each one, leading to the exploration of existing limitations of ML or DL-enhanced models. Discovering the limitation is crucial for future work and research to increase the effectiveness and robustness of various AI models. The key finding from this study demonstrates the need for continuous development of AI models that could be alert to the latest fraudulent activities.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1186/s40537-024-01048-8
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