AI Authorization System Personalized Fraud Thresholds
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Solution Overview
Problem
Conventional fraud-scoring mechanisms in financial authorization systems often result in substantial losses due to false positives, where legitimate transactions are declined, leading to customer dissatisfaction and loss of business, while false negatives allow fraudulent transactions to be approved, and existing systems fail to consider the behavioral impacts on customers and the overall business profitability.
Innovation Solution
A business method that reverses authorization request denials for customers and transactions that pass specific threshold tests, with personalized thresholds for each customer based on transaction types, amounts, times, locations, and context, using artificial intelligence and machine-learning services to provide on-demand data science and predictive modeling for improved decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fraud-scoring mechanisms treat all customers the same, then fraud detection consistency is maintained, but false positives increase and customer satisfaction decreases
Solution Approach 1:
The patent implements personalized fraud scoring thresholds for different customers based on their individual behavior patterns, transaction history, and risk profiles. Instead of applying a uniform fraud scoring mechanism to all customers, the system tailors the scoring criteria and thresholds to each customer's specific characteristics, thereby reducing false positives for legitimate customers while maintaining fraud detection effectiveness.
Solution Approach 2:
The system dynamically adjusts fraud scoring thresholds and parameters based on real-time behavioral analysis and historical data. The fraud detection mechanism evolves and adapts to each customer's changing behavior patterns, allowing the system to respond flexibly to new transaction types and emerging fraud patterns while maintaining consistency in its protective function.
2Object-affected harmful factors
If fraud scoring mechanisms aggressively decline suspicious transactions, then fraudulent transactions are blocked, but legitimate business revenue is lost due to false positives
Solution Approach 1:
The patent changes the key parameter of fraud scoring thresholds from a fixed, uniform value to dynamic, customer-specific values. By adjusting these thresholds based on individual customer risk profiles and behavioral patterns, the system optimizes the balance between blocking fraudulent transactions and allowing legitimate business transactions to proceed, thereby minimizing revenue loss from false positives.
Solution Approach 2:
The system performs preliminary behavioral analysis and pattern recognition before making authorization decisions. By pre-establishing customer behavior baselines and risk profiles, the system can make more accurate real-time decisions about whether to approve or decline transactions, reducing the number of legitimate transactions incorrectly flagged as fraudulent.
3Device complexity
If uniform authorization thresholds are applied to all customers, then system simplicity is maintained, but business profitability decreases due to inability to accommodate high-value customers
Solution Approach 1:
The patent introduces customer-specific authorization thresholds and scoring parameters that are tailored to individual customer profiles. High-value customers with established trust profiles receive more favorable authorization terms and higher thresholds, while maintaining the core authorization system's overall structure. This localized customization improves profitability by accommodating valuable customers without requiring complete system redesign.
Solution Approach 2:
The authorization system segments customers into different risk categories and applies differentiated threshold levels to each segment. This segmentation allows the system to maintain simplicity at the aggregate level while implementing complexity only where necessary for individual customer groups, thereby improving profitability without overwhelming system complexity.
Data Source
AI summary
A business method to reverse an authorization request denial made according to general guidelines if the particular customer and the particular transaction pass various threshold tests. Alternatively, each customer is assigned different and independent personal thresholds for transaction types, amounts, times, locations, and context. These thresholds are then applied if an instant payment transaction request is about to be declined.