The Convergence of Artificial Intelligence and Machine Learning in Credit Card Fraud Detection: A Comprehensive Study on Emerging Trends and Advanced Algorithmic Techniques

Lakshminarayana Reddy Kothapalli Sondinti; Chandrashekar Pandugula

Authors

Lakshminarayana Reddy Kothapalli Sondinti Sr software engineer, US bank, Dallas USA Author
Chandrashekar Pandugula Sr Data Engineer, Lowes Inc NC, USA Author

Keywords:

Artificial Intelligence, Machine Learning, Credit Card Fraud Detection, Deep Learning, Anomaly Detection, Ethical Considerations, Cross-Validation, Artificial Intelligence in Fraud Prevention, Machine Learning Algorithms, Fraud Detection Systems, Emerging Trends in Fraud Detection, Advanced Fraud Detection Techniques, AI and ML Convergence, Predictive Analytics in Fraud Detection, Data-Driven Fraud Prevention, Anomaly Detection in Financial

Abstract

Artificial Intelligence (AI) is generating an unprecedented amount of data around the world, and this data is used to improve the lives of human beings through various applications. One of the most rapidly emerging application areas is credit card fraud detection, which requires a lot of attention due to its significant financial impact. In the realm of AI, machine learning (ML) has also shown potential for handling big data in an advanced and intelligent way. ML provides various types of techniques that can be used to build a fraud detection module with improved performance. Machine learning-based models for detecting fraud can be divided into three main categories: supervised learning, unsupervised learning, and semi-supervised learning. Over the past few years, the potential use of fraud detection radar systems using supervised learning and unsupervised learning methods, such as deep learning, transfer learning, multilayer perceptron, and convolutional neural networks, has gradually emerged.

In a nutshell, the aim of the current paper is twofold. The first is to investigate the initial advantages of ML techniques, such as financial and business relevance. The second objective is to explore the future time when AI will be used in the domain of credit card fraud detection. As many organizations are looking to AI to make fraud detection much faster, we are at a unique place where risk management systems can be leveraged to provide a better fraud detection experience for card owners while also reducing the dollar amount of fraud. In addition to this, emerging trends in fraud detection regulatory agencies have started revamping model risk management policies in the past three years. It will also certainly be a point of future interest in raising AI for fraud detection alongside traditional supervised and unsupervised learning.

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