Price Comparison System Fraud Detection and Credit Assignment
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Solution Overview
Problem
Consumers and retailers face difficulties in evaluating competitive pricing across various retail stores, leading to inconvenient and time-consuming processes for finding the best deals, as existing methods lack efficient price comparison and fraud detection in transactions.
Innovation Solution
A system that utilizes a server system to compare transaction records with third-party pricing data, credits users for price differences, and employs fraud detection methods by analyzing purchasing patterns and historical data to flag potentially fraudulent transactions, allowing for automatic or manual validation and applying credits towards subsequent purchases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customers manually review advertisements and coupons from various media to find the best deals, then they can identify price differences, but the process becomes time-consuming and inconvenient
Solution Approach 1:
The system enables automatic price monitoring and comparison without requiring customer intervention. The server system automatically retrieves pricing data from multiple retailers, compares prices, and notifies customers of deals, allowing the system to serve itself rather than requiring manual customer effort.
Solution Approach 2:
A server system acts as an intermediary between customers and multiple retailers. This intermediary automatically collects pricing information from various sources, processes the data through comparison algorithms, and presents consolidated results to customers, eliminating the need for customers to manually search through multiple advertisements.
2Ease of operation
If the system credits users for price differences without verification, then customer satisfaction increases, but fraudulent transactions may occur
Solution Approach 1:
The system implements feedback mechanisms where credit assignments are monitored and evaluated. Transaction data is continuously analyzed to detect patterns that may indicate fraud, and the system adjusts its credit assignment behavior based on this feedback, balancing ease of operation with transaction security.
Solution Approach 2:
The system performs preliminary verification of transactions before assigning credits. By analyzing purchase history, transaction patterns, and product data in advance, the system identifies potentially fraudulent transactions before credits are issued, preventing fraud while maintaining operational simplicity for legitimate transactions.
3Measurement precision
If the system monitors all transaction data for fraud detection, then fraud detection accuracy improves, but system complexity increases
Solution Approach 1:
The fraud detection system is segmented into modular components that handle different aspects of analysis separately. Transaction monitoring, pattern recognition, and credit verification are divided into distinct functional modules, allowing the system to achieve high detection accuracy while managing complexity through modular architecture.
Data Source
AI summary
Systems and methods are disclosed for evaluating a transaction concluded at a POS (point of sale) device. Prices for competitive retail stores within a geographic region of the POS may be evaluated after concluding a transaction. Price differences between items and corresponding prices in the third party data are identified. Where the purchase price exceeds the corresponding third-party price, a credit is assigned to the customer, such as in the form of a gift card or code that may be redeemed in a subsequent transaction. Credits may also be assigned to a debit card associated with a user, either with or without applying some multiplier. Transactions may be compared to past transaction of a user in order to detect fraud. Recent activity may be flagged as potentially fraudulent and reviewed before providing a credit.