Fraud Detection System Using Service Deterioration
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Solution Overview
Problem
Fraudsters can easily analyze and bypass existing fraud detection tools in e-commerce and online services by understanding how they work, leading to increased fraudulent activities due to the need for real-time responses and quick account setup, which allows them to evade detection and continue their malicious behavior.
Innovation Solution
Implementing a method and apparatus that analyze data transactions using predefined fraud detection rules to generate a fraud score, classifying accounts as fraudulent if the score exceeds a threshold, and instead of blocking them, intentionally degrading the service or product offered to these accounts, making it difficult for fraudsters to achieve a positive return on investment and continue their fraudulent activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time fraud detection is implemented to quickly identify fraudulent accounts, then fraud detection speed is improved, but fraudsters can easily analyze and bypass the detection tools by understanding their behavior patterns
Solution Approach 1:
Instead of blocking fraudulent accounts immediately (traditional approach), the system intentionally allows them to proceed with deteriorated service quality. This inverts the conventional fraud response from prevention to controlled permission, making it difficult for fraudsters to analyze detection patterns while maintaining security.
Solution Approach 2:
The system changes the service quality parameter for fraudulent accounts rather than using binary block/allow decisions. By introducing service deterioration as a intermediate state, the system maintains detection effectiveness while preventing fraudsters from learning detection patterns through complete blocks.
2Reliability
If fraudulent accounts are completely blocked to prevent fraud, then fraud prevention effectiveness is improved, but service quality for legitimate users may be impacted and fraudsters can adapt by analyzing block patterns
Solution Approach 1:
The system inverts the traditional fraud response by not blocking fraudulent accounts but instead allowing them with deteriorated service. This prevents fraudsters from analyzing block patterns while maintaining fraud prevention through service degradation that reduces fraudsters' return on investment.
Solution Approach 2:
The system converts the harmful effect of allowing fraudulent transactions into a benefit by deliberately deteriorating service quality for fraudulent accounts. This reduces the value of fraudulent activities while maintaining the ability to detect and respond to fraud attempts.
3Ease of operation
If service is continuously provided to all accounts without differentiation, then user experience for legitimate users is maintained, but fraudulent activities can proceed unchecked
Solution Approach 1:
The system applies different service qualities to different accounts based on their fraud risk classification. Legitimate users experience normal service quality while fraudulent accounts receive deteriorated service, allowing the system to protect against fraud while maintaining excellent user experience for legitimate users.
Solution Approach 2:
The system changes service quality parameters dynamically based on fraud detection results. By adjusting service parameters (such as response time, feature availability, or transaction limits) for specific accounts, the system maintains ease of operation for legitimate users while reducing the effectiveness of fraudulent activities.
Data Source
AI summary
The present disclosure relates to a concept of fraud handling. A data transaction request is received via a data network from at least one user account. The data transaction request is analyzed based on predefined fraud detection rules to generate a fraud score associated with the at least one user account. The at least one user account is classified as fraudulent account if the associated fraud score exceeds a predefined fraud likelihood threshold. Data transactions associated with a classified fraudulent account via the data network are done by purposely deteriorating the data transactions associated with the classified fraudulent account in comparison to data transactions associated with a classified non-fraudulent account.