Fraud Detection System Identifying Transaction Origins
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Solution Overview
Problem
Users face difficulties in identifying the origin of fraudulent financial transactions across multiple accounts, making it hard to pinpoint where their account information was stolen and warning others who may have been affected simultaneously.
Innovation Solution
A fraud detection system that analyzes financial transaction data from multiple users to identify potential origins of fraud by determining intersections in payee and time, storing this information in a fraud alert table, and alerting affected users to take preventive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users review their own financial transactions to identify fraud, then they can detect fraudulent charges in their account, but they cannot determine the origin of the fraud or warn others affected at the same time and location
Solution Approach 1:
The patent merges transaction data from multiple users into a centralized financial transaction table, combining information about payees, timestamps, and user accounts. This aggregation enables the system to identify patterns and origins of fraud that individual users cannot detect alone, while preserving the ability to trace fraud back to specific locations and times.
Solution Approach 2:
The system introduces an intermediary fraud detection service that collects, analyzes, and correlates transaction data from multiple users. This intermediary layer processes the raw transaction information, identifies fraudulent patterns through intersection analysis, and provides origin information back to users, solving the information loss problem without requiring users to directly share sensitive data.
2Loss of information
If the system collects and analyzes financial transaction information from multiple users to identify fraud origins, then it can pinpoint the source of account information theft, but it increases system complexity and data processing requirements
Solution Approach 1:
The system segments the fraud detection process into distinct functional modules: data collection from multiple users, storage in a standardized financial transaction table, intersection analysis to identify common payees and time windows, and result dissemination. This segmentation reduces overall system complexity by making each component manageable and independently implementable.
Solution Approach 2:
The system performs preliminary actions by pre-processing and standardizing financial transaction data from multiple users before analysis. Transaction information is normalized into a consistent format with standardized fields for payees, timestamps, and user accounts, and stored in a centralized table. This preliminary organization simplifies subsequent intersection analysis and reduces the complexity of real-time fraud detection.
3Measurement precision
If the system analyzes detailed financial transaction entries including payee and time information, then it can identify potential origins of fraud through intersection analysis, but it increases data processing time and computational resources
Solution Approach 1:
The system changes parameters by focusing analysis on specific key fields such as payee names and time windows rather than processing entire transaction records. By identifying and analyzing only the relevant intersection points (common payees within specific time periods), the system maintains high identification accuracy while significantly reducing data processing time and computational resource requirements.
Data Source
AI summary
A method for identifying an origin of fraudulent activity including receiving a first affirmative fraud flag from a first user for a financial transaction associated with a first user account and obtaining financial transaction information for the first user account, which includes a first financial transaction entry listing a payee and a first time. The method further includes receiving a second affirmative fraudulent flag from a second user corresponding to a financial transaction and a second user account and obtaining financial transaction information for the second user account, which includes a second financial transaction entry listing the payee and a second time. The method further includes determining an intersection between the first and the second financial transaction entries, identifying a potential origin of fraud from the intersection, and alerting the first and the second user of the potential origin of fraud.