Financial Crime World

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Credit Card Fraud Detection Methods in Hong Kong

The Rise of Credit Card Fraud in Hong Kong

With the increasing number of digital transactions, credit card fraud cases have been on the rise in Hong Kong. Financial institutions are turning to advanced technologies such as artificial intelligence and machine learning to detect and prevent credit card fraud.

Current Challenges in Credit Card Fraud Detection

Existing studies have focused on improving the performance indicators of existing fraud detection methods using specific datasets. However, there is a lack of methodological research on how to design models that can achieve optimal performance in new tasks.

Designing Effective Financial Loan Fraud Detection Models

In this article, we propose effective design ideas for financial loan fraud detection models by considering multiple types of customer behavior data simultaneously. Our experimental results demonstrate the correctness of our model design method.

Key Considerations in Model Design

  • Customer Basic Behavior Data: This includes information about customer purchase history, transaction frequency, and other basic behaviors.
  • Relationship Breadth Information: This refers to the variety of relationships a customer has with different merchants or financial institutions.
  • Relationship Depth Information: This involves the intensity of relationships between customers and merchants or financial institutions.
  • Relationship Connection Tendency Information: This indicates the likelihood of a customer’s connections with other individuals or entities.

Experimental Results

Our experimental results show that incorporating all four types of information leads to better performance compared to methods that focus on a single type of information. This suggests that considering multiple sources of data can lead to more accurate and effective credit card fraud detection in Hong Kong.

Contribution to the Literature

This study contributes to the existing literature by providing insights into the design of financial loan fraud detection models. We also highlight the importance of considering multiple types of information in order to achieve optimal performance in credit card fraud detection.

References

This article is based on research presented at the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence.