Stored-value cards are a popular payment method that uses a magnetic strip or RFID chip to store value. They are typically used for one-time purchases and are not associated with a financial institution or credit limit. Stored-value cards are projected to reach a global market size of $5.51 trillion by 2027, driven by the demand for alternatives to cash and cheques, the growth of e-commerce, and the increasing number of internet users. However, the industry is also vulnerable to fraudulent attacks.
Fraud is difficult to detect because it is rare, planned and well-executed, concealed, and time-evolving. Fraudsters often operate in large teams and use sophisticated techniques to conceal their activities. As fraudsters evolve their techniques, fraud detection systems must also evolve to keep up.
Traditional rule-based fraud detection systems are becoming difficult to maintain and manage as the number of vendors and business rules increases. Machine learning (ML) techniques offer several advantages in fraud detection, including:
ML fraud detection systems offer several advantages over traditional rule-based systems, including:
ML fraud detection approach uses supervised learning techniques to train classifiers that can identify fraudulent transactions with high accuracy. The approach also incorporates unsupervised learning to handle concept drift, ensuring that the model remains effective over time.