Fraud Network Data Quality Feedback Loop
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Solution Overview
Problem
Existing electronic fraud networks face challenges with low-quality and inaccurate data provided by customers, including improper formats and erroneous classifications, which affect the reliability of fraud detection systems.
Innovation Solution
Implementing an automatic data quality feedback loop that evaluates the accuracy of input data, updates trust measures for data sources, and provides feedback to customers, ensuring higher quality data is provided by weighing and analyzing input data based on historical accuracy and format validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customers provide data to the EFN, then the EFN can generate unified fraud detection lists, but the data quality is low and inaccurate
Solution Approach 1:
The patent implements an automated feedback loop where the EFN evaluates the quality of submitted data and communicates quality assessments back to customers. This feedback mechanism enables customers to understand why their data was rejected or flagged, allowing them to improve future submissions and progressively enhance overall data quality in the system.
2Adaptability or versatility
If the EFN accepts data from multiple sources, then more fraud information is available, but data format inconsistencies and errors increase
Solution Approach 1:
The patent employs automated validation rules that check multiple parameters of submitted data including format compliance, required field presence, and data type correctness. The system dynamically adjusts acceptance criteria based on data source trust levels and provides specific parameter-level feedback to guide customers in correcting format issues while maintaining compatibility across diverse sources.
3Reliability
If the EFN processes all submitted data, then comprehensive fraud coverage is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent implements a tiered processing approach where data from high-trust sources undergoes streamlined validation with reduced processing steps, while data from lower-trust sources receives more intensive scrutiny. This partial action strategy maintains comprehensive fraud detection coverage by appropriately allocating processing resources based on source reliability assessments.
4Manufacturing precision
If the EFN implements strict data validation, then data quality improves, but more data is rejected and fewer sources can contribute
Solution Approach 1:
The patent provides pre-submission validation templates and guidance materials to customers before they submit data. This preliminary action helps customers prepare properly formatted, high-quality data in advance, reducing rejection rates and enabling more sources to participate successfully in the EFN while maintaining strict quality standards.
Data Source
AI summary
Methods, apparatus and articles of manufacture for providing an automatic electronic fraud network data quality feedback loop are provided herein. A method includes evaluating an item of input data provided by a given source, wherein the item of input data comprises a fraud-related status identifier provided by the given source, and wherein said evaluating comprises determining a level of accuracy associated with the fraud-related status identifier; outputting the determined level of accuracy associated with the fraud-related status identifier to the given source; and updating a trust measure associated with the given source based on the determined level of accuracy associated with the fraud-related status identifier provided by the given source.