DESIGN AND IMPLEMENTATION OF A CREDIT CARD FRAUD DETECTION SYSTEM
Abstract : Credit card fraud detection is a relevant problem that draws attention of machine learning. In the fraud detection task, there are some peculiarities present, such as the unavoidable condition of a strong class imbalance, the existence of unlabelled transaction, and the large number of records that must be processed.
The present paper aims to propose a methodology for automatic detection of fraudulent transactions that tackle all these problems. The methodology is based on Balanced Random Forest that can be used in supervised and semi-supervised scenarios through a co-training approach, which allows to compensate the class imbalance problem.
In FDS, it has described the alert feedback interaction, which is a mechanism of providing recent supervised samples to train or update the classifiers. The main objective of FDS is to identify the fraud as soon as possible in order to take the necessary actions to revert it.
Feedbacks play a central role in the proposed learning strategy.
credit card fraud detection systems utilize a variety of methodologies to protect against fraudulent transactions, each with its own strengths and limitations. Traditional rule-based systems apply predefined criteria to flag suspicious transactions but can struggle with new or evolving fraud tactics.
Machine learning-based approaches, including supervised and unsupervised models, offer more adaptability by learning from historical data and identifying patterns that signal fraud.
Supervised learning models, such as logistic regression and random forests, are effective for known fraud patterns, while unsupervised techniques, like anomaly detection, can uncover novel fraud schemes without needing labeled examples.
Deep learning systems further enhance detection capabilities by modeling complex data relationships through neural networks, though they require substantial computational resources. Real-time processing systems handle high transaction volumes efficiently, enabling immediate fraud detection.
Data-Related Challenges
Data Privacy: Handling sensitive financial data requires strict adherence to privacy regulations (e.g., GDPR, CCPA). This can complicate data collection, sharing, and processing.
Data Imbalance: Fraudulent transactions are relatively rare compared to legitimate transactions. This class imbalance can lead to models that are biased towards predicting non-fraudulent transactions.
Data Quality: Incomplete, noisy, or incorrect data can affect model performance. Data from different sources might not be standardized, complicating preprocessing.
Model and Algorithm Limitations
False Positives and Negatives: Models may generate false positives (legitimate transactions flagged as fraud) and false negatives (fraudulent transactions missed). High false positive rates can lead to customer dissatisfaction, while high false negative rates can result in undetected fraud.
Overfitting: Models might overfit to historical data, performing well in training but poorly on new, unseen data. This limits their ability to generalize to evolving fraud patterns.
Complexity: Some advanced algorithms (e.g., deep learning models) require significant computational resources and can be difficult to interpret, making them less suitable for real-time applications or regulatory compliance.
Enhanced Security
Fraud Prevention: The primary advantage is the ability to identify and prevent fraudulent transactions in real-time or near-real-time, thereby reducing financial losses and protecting sensitive customer information.
Early Detection: Advanced systems can detect unusual patterns and emerging threats quickly, minimizing the impact of fraud.
Improved Customer Trust
Customer Protection: By preventing unauthorized transactions and protecting account details, financial institutions can build and maintain trust with their customers.
Reduced Fraudulent Charges: Customers are less likely to face financial losses due to fraud, leading to higher satisfaction and loyalty.
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