Data Quality Assurance in Credit Card Fraud Detection

In 2023, Credit card fraud cases soared to 425,977, up 53% from 2019 driven by the surge in online shopping amid the COVID-19 pandemic. This underscores the pressing need for advancing Fraud Detection techniques, heavily relying on data analysis and modeling.

While modeling techniques are widely discussed in academia, data quality assurance for fraud detection is a less popular topic. However, with the challenge of data imbalance and the manual components in fraud labeling, data quality assurance becomes even more important to ensure the quality of fraud detection. This article discusses metrics, challenges, and solutions of data quality assurance in fraud detection, addressing:

• Impact of data quality on credit card fraud detection
  
• Definition and dimensions of data quality in credit card fraud detection
  
• Key components of data quality assurance in credit card fraud detection
• Challenges specific to fraud detection data quality assurance
• The latest industry solutions and best practices
  

Besides reviewing recent academic research papers, this article incorporates industry research reports, technical blogs from leading FinTech companies, and vendor product specifications. It focuses on actionable insights for data quality assurance in credit card fraud detection.

1. Impact of data quality on credit card fraud detection
   
2. Definition and dimensions of data quality in credit card fraud detection
   
3. Key components of data quality assurance in credit card fraud detection
   
4. Challenges specific to fraud detection data quality assurance
   
5. The latest industry solutions and best practices
   

Yuqing Yao

Yuqing Yao currently works as a Senior Fraud Analyst at a FinTech company, Klarna. She uses statistical approaches and visualization to detect fraud patterns and program them in the fraud decisioning system. Prior to that, she worked as Assistant Director in Data Operations at Moody's Analytics, leading the data processing and reporting of its credit data consortium. She started her career in Treasury function at Stripe, doing liquidity forecasting and monitoring. Her career interests have been in data science, analytics, and risk management. In her spare time, she actively volunteers for organizing AI/ML meetups in San Francisco. Besides, she is a long-term Toastmasters member with awards in speech contests.