Merchant Location Correction via Customer Transaction Analysis
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Solution Overview
Problem
Existing payment systems face challenges in accurately verifying the location of merchants and customers, leading to potential fraud and mismatched zip codes, which can result in lost sales or fraudulent transactions.
Innovation Solution
A method and system that compares the reported merchant zip code to the frequently shopped zip codes of customers using various statistical and machine learning algorithms to determine if the variance exceeds a threshold, triggering alerts or actions to correct the merchant's location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If merchants report their own zip codes or locations, then the setup process is simple and quick, but the accuracy and reliability of the location information cannot be guaranteed
Solution Approach 1:
The system performs preliminary actions by collecting customer zip codes from card-present transactions before making a determination about merchant location accuracy. Historical transaction data is gathered and analyzed in advance to establish the frequently shopped zip code, which is then used to verify the reported merchant location.
Solution Approach 2:
The system uses feedback from customer transaction data to verify and potentially correct the reported merchant zip code. The frequently shopped zip code derived from customer transactions serves as feedback to validate whether the merchant's reported location is accurate, creating a self-verifying system.
2Reliability
If strict zip code matching is enforced between merchants and customers, then fraudulent transactions can be reduced, but legitimate transactions may be rejected
Solution Approach 1:
The system applies local quality by determining the merchant's frequently shopped zip code specific to each merchant's customer base, rather than using a universal zip code matching rule. This localized approach allows each merchant to have their own verified location profile, enabling more nuanced fraud detection that considers the specific geographic patterns of each merchant's customers.
Solution Approach 2:
The system changes the parameter for location verification from a simple reported zip code to a statistically derived frequently shopped zip code. This parameter change allows for more flexible matching, as the system can identify legitimate transactions based on the established geographic pattern rather than requiring exact matches with a potentially incorrect reported location.
3Adaptability or versatility
If inaccurate merchant zip codes are allowed, then merchants may gain unfair advantages in certain locations, but this compromises the integrity of the payment system
Solution Approach 1:
The system performs preliminary verification by analyzing historical transaction data to establish the true frequently shopped zip code before allowing the merchant to operate with that location. This preliminary action ensures that the payment system maintains integrity by verifying locations based on actual customer transaction patterns rather than accepting unverified merchant-reported locations.
Data Source
AI summary
In one embodiment, the location or zip code of purchasers in a card present electronic transaction may be reviewed and compared to the reported location or zip code of a merchant. If the difference or variance from the customer zip code or location to the customer location or zip code is over a threshold, the merchant is indicating as requiring additional attention.