Pseudo-Random Fraud Transaction Blocking
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Solution Overview
Problem
Fraudsters can easily analyze and bypass existing fraud detection tools in e-commerce and similar data transactions by understanding their behavior patterns, leading to increased fraudulent activities due to the real-time nature of service responses.
Innovation Solution
Implementing a method and apparatus that pseudo-randomly permit or block data transactions based on fraud scores, using predefined fraud detection rules and machine learning models, to obscure the behavior of fraud detection tools, thereby making it harder for fraudsters to identify and circumvent the detection mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time fraud detection is implemented, then user experience and transaction speed are improved, but fraudsters can easily analyze and bypass detection tools by understanding their behavior patterns
Solution Approach 1:
The system dynamically changes its fraud detection behavior by introducing randomness in the blocking decisions. Instead of consistently blocking transactions with fraud scores above a threshold, the system pseudo-randomly permits or blocks transactions, making the detection pattern unpredictable and adaptable to evolving fraud tactics
Solution Approach 2:
The system changes the parameter of transaction outcome from deterministic to probabilistic. By introducing a pseudo-random element in the blocking decision process, the system transforms the fixed threshold-based outcome into a dynamic parameter that varies even for identical fraud scores, obscuring the detection mechanism
2Reliability
If deterministic fraud blocking is used, then fraud detection accuracy is improved, but fraudsters can reverse engineer the tools and create strategies to bypass detection
Solution Approach 1:
The system makes the fraud detection mechanism dynamic by introducing pseudo-randomness in the blocking decision. This dynamic behavior prevents fraudsters from reverse engineering a fixed pattern, as the same fraud score can lead to different outcomes (block or permit), thereby maintaining adaptability against evolving fraud tactics
Solution Approach 2:
The pseudo-random decision mechanism acts as an intermediary between the fraud score and the final blocking decision. This intermediary layer obscures the direct relationship between detection rules and outcomes, making it difficult for fraudsters to understand and bypass the detection tool while maintaining detection accuracy
3Object-affected harmful factors
If all high fraud score transactions are blocked, then fraud prevention is improved, but legitimate transactions may be incorrectly blocked and user experience degraded
Solution Approach 1:
Instead of deterministically blocking all transactions above a threshold, the system applies partial blocking by pseudo-randomly selecting which high-risk transactions to block. This partial action reduces the impact on legitimate transactions while still maintaining strong prevention against fraudulent ones, as fraudsters cannot reliably identify which transactions will be blocked
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 pseudo-randomly permitted or blocked.