Fraud Analysis in Contact Databases Using Pattern Recognition
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Solution Overview
Problem
Current systems face challenges in efficiently identifying and flagging fraudulent data in contact databases, particularly in large datasets, where manual verification is cumbersome and resource-intensive.
Innovation Solution
A computer-implemented method for fraud analysis in contact databases that involves determining similar and unusual content patterns in contact records, using threshold values, dupes-scores, and unusualness scores to flag potentially fraudulent data, thereby reducing the need for extensive manual review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification is used to identify fraudulent data, then accuracy in detecting bogus information is improved, but productivity and efficiency deteriorate due to cumbersome and resource-intensive processes
Solution Approach 1:
The patent introduces automated fraud detection algorithms as an intermediary between manual verification processes. These algorithms analyze contact records for suspicious patterns (duplicate emails, inconsistent data, unusual formatting) and flag potential fraud cases, allowing manual reviewers to focus only on flagged records rather than examining every submission, thus maintaining accuracy while improving productivity
Solution Approach 2:
The system implements self-service fraud detection by automatically analyzing contact records upon submission. The fraud detection algorithms independently evaluate data quality, identify suspicious patterns, and generate fraud risk scores without requiring immediate manual intervention, enabling the system to handle large volumes of records efficiently while maintaining detection accuracy
2Reliability
If extensive manual review is performed on all contact records, then reliability in identifying fraudulent data is improved, but loss of time and resources increases significantly
Solution Approach 1:
The patent applies partial action by performing automated fraud detection on all records but requiring manual review only for records exceeding a fraud risk threshold. This selective approach ensures reliable detection of high-risk fraudulent data while minimizing time loss by avoiding extensive manual review of low-risk records
Solution Approach 2:
Automated fraud detection algorithms serve as an intermediary filtering layer that pre-screenes all contact records before they reach manual reviewers. The system calculates fraud risk scores and flags only suspicious records for manual examination, maintaining reliability for fraudulent data identification while dramatically reducing the time investment required for verification
3Productivity
If automated fraud detection algorithms are implemented, then productivity and efficiency are improved, but device complexity increases due to pattern analysis requirements
Solution Approach 1:
The patent segments the fraud detection process into distinct modular components: data validation module (checking format and completeness), pattern recognition module (identifying duplicate emails and suspicious sequences), statistical analysis module (calculating fraud risk scores), and flagging module (marking suspicious records). This segmentation improves productivity through automated parallel processing while managing complexity by organizing functions into separate, maintainable modules
Solution Approach 2:
The fraud detection system is designed as a universal multi-functional platform that handles multiple fraud detection tasks (duplicate detection, pattern recognition, statistical analysis, flagging) within a single integrated architecture. This universality improves productivity by processing all fraud detection functions in one pass through the contact records while managing complexity through a unified system design rather than separate systems for each function
4Measurement precision
If pattern analysis is performed on all contact field values, then measurement precision in detecting fraudulent data is improved, but use of energy and computational resources increases
Solution Approach 1:
The patent applies local quality by performing intensive pattern analysis only on specific high-risk contact fields (email addresses, phone numbers, company names) rather than uniformly analyzing all fields. The system identifies and focuses computational resources on fields with higher fraud risk, improving detection precision for critical data while reducing overall energy consumption by avoiding exhaustive analysis of low-risk fields
Data Source
AI summary
A system and method of identifying fraudulent data in a contact database is disclosed herein. In some embodiments, a set of contact records is received where each of the contact records includes a set of contact field values corresponding to a set of contact fields. Some embodiments determine whether a similar content pattern exists in the contact records using at least one of the set of contact field values. In some embodiments, a determination is made as to whether an unusual content pattern exists in the contact records using at least one of the set of contact field values. The set of contact records is flagged when at least one of the similar content pattern or the unusual content pattern is determined to exist in the contact records.