Transaction Ticket Size Pattern Fraud Detection
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Solution Overview
Problem
Current methods for detecting fraudulent transactions in payment card systems are limited, as they can be vulnerable to forged signatures, compromised PINs, and false IDs, failing to effectively identify unauthorized use based on spending patterns.
Innovation Solution
A computer-implemented method and system that analyze transaction information by comparing current transaction amounts to historical spend ticket size patterns, including average ticket size and dispersions, to detect deviations and recommend approval or decline of transactions, utilizing a transaction ticket size pattern module within a payment card network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods (signatures, PINs, IDs) are used to verify cardholder identity, then basic security against unauthorized use is provided, but these methods are vulnerable to forgery, guessing, or compromise
Solution Approach 1:
The patent replaces mechanical/authentication-based verification systems (signatures, PINs, physical IDs) with an automated computational system that analyzes transaction patterns using algorithms and historical data. The system substitutes human verification processes with machine learning models that detect anomalies in spending behavior, thereby eliminating vulnerabilities to physical forgery and compromise.
Solution Approach 2:
The system enables the cardholder's own spending patterns to serve as the authentication mechanism. By establishing baseline spending behavior through historical transactions and automatically comparing current transactions against these patterns, the system uses the cardholder's behavioral data to detect fraud without requiring additional authentication inputs from the user.
2Measurement precision
If the system analyzes detailed transaction patterns to detect fraud, then fraud detection accuracy improves, but system complexity increases
Solution Approach 1:
The system performs preliminary analysis by establishing spending patterns and baselines during normal transaction processing before fraud occurs. Historical transaction data is continuously analyzed to build profiles of normal spending behavior, so when a transaction occurs, the fraud detection comparison is already prepared and can quickly evaluate whether the current transaction deviates from established patterns.
Solution Approach 2:
The system applies fraud detection analysis selectively rather than uniformly to all transactions. It focuses computational resources on transactions that show deviations from established patterns or occur in contexts that warrant closer scrutiny, rather than analyzing every single transaction in exhaustive detail, thereby balancing accuracy with system complexity.
Data Source
AI summary
A method and system for detecting fraud in a payment card network using a pattern of transaction ticket size are provided. The method including receiving transaction information, for a current financial transaction, from at least one of a merchant point of sale (POS) device and a merchant website, the transaction information including a current transaction amount, the transaction information associated with a single payment card cardholder, retrieving a predetermined number of historical transactions for the single cardholder based on the transaction information, and generating a historical spend ticket size pattern based on average ticket size and dispersions for at least one of the same store, similar stores, and relevant merchant categories. The method further including comparing the current transaction amount to the historical spend ticket size pattern and generating a recommendation for approval or decline of the current financial transaction based on the comparison.