This chapter explores using auto-encoders and attention mechanisms to enhance privacy-preserving capabilities of federated machine learning for credit card fraud detection. The proposed system uncovers latent features in credit card transactions, leveraging distributed training for user privacy. Empirical evaluation on real-world data shows its proficiency in identifying fraud. The method offers privacy preservation, scalability, and resilience. The model's performance across imbalanced and balanced datasets highlights the role of balanced data in optimizing fraud detection. This approach integrates accuracy, privacy preservation, and security. Considering fraudsters' sophistication, this research introduces a strategy to counter credit card fraud while preserving confidentiality. In summation, this chapter presents a framework for deploying privacy-focused federated machine learning in credit card fraud detection and prevention, fostering privacy-preserving applications across domains.
Top1. Introduction
The prevalence of e-commerce and online payment methods in recent years has resulted in a significant increase in fraudulent transactions. Reports indicate a substantial rise in fraud losses related to credit and debit cards between 2000 and 2015. Notably, unauthorized purchases and counterfeit credit cards, although constituting a smaller proportion of total fraud cases, account for a significant majority (75-80%) of the financial value involved (Saia & Carta, 2019). In response to these challenges, both private and public entities have increased investments in research and development to develop more effective fraud detection systems.
Credit cardholders are encouraged to use their cards for payments due to the convenience and speed they offer, facilitating both national and international transactions, including cash withdrawals (Al Rubaie, 2021). However, along with these benefits come increased risks, particularly related to credit card fraud. Detecting fraudulent transactions has become a challenging task, especially with the rise of online businesses that exclusively accept credit card transactions, leading to a surge in fraudulent activities. The need for robust fraud detection and prevention techniques has become crucial due to the increasing trend of credit card online transactions. In 2020, there were approximately 2,183 million Visa and Mastercard users worldwide, with over 30% of Visa card users and 24% of Mastercard users located in the United States (Alkhatib, 2021). This surge in cashless transactions has resulted in a corresponding increase in fraud rates for Card Not Present (CNP) transactions (Murli, 2015). Furthermore, the COVID-19 pandemic has accelerated the shift towards online transactions, emphasizing the urgency to effectively prevent and detect fraud. Consequently, researchers and financial institutions are continuously seeking more efficient techniques to enhance the safety of online transactions, with machine learning (ML) playing a pivotal role in this endeavor.
For financial institutions that issue credit cards or handle online transactions, the implementation of automated fraud detection systems is crucial. These systems play a vital role in reducing losses and fostering customer trust. With the advent of big data and artificial intelligence, new opportunities have emerged for utilizing advanced machine learning models to detect fraudulent activities (Bao et al, 2022). Current fraud detection systems that leverage advanced data mining and machine/deep learning methods have proven highly effective.
Typically, these systems employ a binary classification model trained on a labeled dataset containing both normal and fraudulent transactions. The model is designed to differentiate between normal and fraudulent transactions, enabling it to make accurate determinations on incoming transactions. However, the task of detecting fraudulent transactions using classification techniques presents several challenges (Fournier & Aloise, 2019). These challenges include class imbalance, cost sensitivity, temporal dependence between transactions, concept drift, and the dimensionality of the feature space. In a comprehensive literature review conducted by Ngai et al, (2011), decision trees, artificial neural networks, logistic regression, and support vector machines were identified as the most frequently used supervised learning techniques for fraud detection. These challenges necessitate the careful consideration of various factors to ensure the effectiveness of fraud detection systems.
Machine learning techniques have proven immensely helpful in fraud detection and prevention efforts. Financial institutions employ ML models to analyze patterns, anomalies, and historical data, effectively identifying potential fraudulent activities. ML techniques have significantly improved the accuracy and efficiency of fraud detection systems. However, credit card fraud detection and prevention pose two crucial considerations: privacy preservation and collaborative learning. Ensuring the privacy of sensitive credit card data is paramount. Privacy-preserving techniques are employed to safeguard individual data points and personally identifiable information (PII), preventing unauthorized access or exposure. Additionally, collaborative learning approaches, such as federated machine learning, enable multiple parties to collaboratively train models without sharing raw data. These techniques mitigate privacy concerns and address data ownership issues.