Discount Eligibility Scoring From Transaction History Signals
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Solution Overview
Problem
Existing systems fail to effectively identify and notify eligible individuals for discounts due to a lack of self-identification and insufficient outreach, leading to a gap between discount availability and utilization.
Innovation Solution
A computer system assesses user eligibility for discounts by analyzing self-identification, transaction history, financial product usage, and geographic location data, employing machine learning algorithms to generate a probability score and facilitate discount application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an opt-in model is used where individuals must actively register and verify their eligibility, then businesses can ensure accurate identification of eligible users, but a significant number of eligible individuals fail to take advantage of discounts due to lack of self-identification
Solution Approach 1:
The patent inverts the traditional opt-in approach by implementing an opt-out model where the system proactively identifies eligible individuals through transaction history analysis and automatically enrolls them in discount programs. Instead of requiring users to actively declare their eligibility, the system infers eligibility from spending patterns and notifies users of their automatic enrollment, thereby resolving the contradiction between accurate identification and high utilization rates
Solution Approach 2:
The system performs self-service by automatically analyzing transaction data, identifying eligible users, and enrolling them in discount programs without requiring manual registration. The system serves itself by using its own transaction processing infrastructure to gather eligibility indicators and automatically apply discounts, eliminating the need for users to take action while maintaining high accuracy in identification
2Productivity
If businesses implement proactive identification systems to reach more eligible individuals, then discount utilization increases, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent leverages the existing multi-functional transaction processing system to perform both traditional payment processing and eligibility analysis simultaneously. The same data infrastructure used for routine transactions is repurposed to gather eligibility indicators, eliminating the need for separate complex analysis systems and reducing overall system complexity while increasing discount utilization
Solution Approach 2:
The system introduces a lightweight intermediary layer that sits between transaction processing and eligibility determination. This intermediary analyzes transaction patterns using simple rules and thresholds rather than complex algorithms, mediating between the existing transaction system and the discount application process, thereby increasing utilization without proportionally increasing complexity
3Measurement precision
If the system analyzes multiple data points including transaction history, financial products, and geographic locations to improve eligibility accuracy, then identification precision increases, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing and categorizing transaction data during routine operations, organizing eligibility indicators in advance before they are needed for discount determination. Transaction patterns are pre-analyzed and stored in a structured format that enables rapid eligibility assessment, reducing processing time while maintaining high accuracy across multiple data points
Solution Approach 2:
The eligibility assessment is segmented into multiple independent evaluation criteria (transaction frequency, geographic patterns, financial product usage) that can be processed separately and independently. Each segment is evaluated using simple, fast rules rather than a single complex analysis, allowing parallel processing that reduces overall computation time while maintaining comprehensive accuracy
Data Source
AI summary
A computer system and method for assessing a probability of a user belonging to a class of users eligible for discount. The method comprises determining if a user has self-identified as belonging to a class of users eligible for discount; determining if the user has received a financial transaction from an organization restricted to the class of users; determining if the user is using a financial product restricted to the class of users; determining if a financial account of the user is linked with a financial institution or account restricted to the class of users; reviewing the transaction history to identify transactions made at locations with access limited to the class of users; identifying recurring transactions with values consistent with applied discounts; and determining a score indicating the probability of the user belonging to the class of users eligible for discount.