Predictive Consumer Behavior Modeling for Payment Transaction Processing
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Solution Overview
Problem
Existing transaction processing systems face increased computational burden and false-positive transaction denials due to excessive use of risk analysis tools, which can deter honest consumers from using the payment processing system.
Innovation Solution
Implementing predictive behavior modeling techniques to generate predictions of consumer transactions, allowing for the reduction of risk analysis applications by comparing proposed transactions to predicted patterns, thereby minimizing unnecessary risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transaction risk analysis is applied to all transactions, then fraud detection capability is improved, but computational burden on payment processors increases
Solution Approach 1:
The system performs preliminary behavior modeling to generate predicted consumer transactions before actual transactions occur. By pre-establishing expected transaction patterns based on historical data and consumer behavior analysis, the system can quickly compare actual transactions against these pre-computed predictions, avoiding the need for complex real-time risk analysis on every transaction while maintaining fraud detection capability.
Solution Approach 2:
The system segments transactions into two categories: those that match predicted consumer behavior patterns and those that deviate from them. For transactions matching predicted patterns, simplified processing is applied. For deviations, full risk analysis is performed. This segmentation allows the system to reduce computational burden on common transactions while maintaining thorough analysis for suspicious transactions.
2Reliability
If transaction risk analysis is applied to all transactions, then fraud detection capability is improved, but false-positive transaction denials increase
Solution Approach 1:
By pre-generating predicted transaction patterns based on established consumer behavior models, the system creates a baseline of expected legitimate transactions. When actual transactions match these pre-established patterns, they are automatically approved without undergoing full risk analysis, thereby eliminating false positives for routine transactions while maintaining fraud detection for anomalies.
Solution Approach 2:
The system applies different levels of analysis to different transactions based on their characteristics. Transactions matching predicted consumer behavior receive simplified local processing with automatic approval, while transactions deviating from patterns undergo comprehensive risk analysis. This localized quality approach ensures thorough scrutiny only where necessary, reducing false positives while maintaining detection capability.
3Productivity
If predictive behavior modeling is implemented, then computational load is reduced, but system complexity increases
Solution Approach 1:
The system creates simplified copies or representations of consumer behavior patterns through predictive modeling. Instead of implementing complex real-time analysis for every transaction, the system uses pre-computed behavioral models that capture essential transaction characteristics. These model copies enable rapid comparison and decision-making, improving processing efficiency while the modeling infrastructure is established once rather than repeatedly.
Data Source
AI summary
An apparatus and method for processing a transaction authorization request to reduce the need for a transaction risk assessment as part of the authorization process. The invention reduces both the data processing burden on the payment processor and the number of transactions for which authorization is denied. In some embodiments, the invention uses predictive or behavior modeling techniques to generate predictions of the transactions the consumer may engage in. If the consumer does engage in a predicted transaction, then a risk analysis process is not performed for that transaction.