Consumer Spending Model Using Balance Transfer Segmentation
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Solution Overview
Problem
Current methods for estimating consumer spend capacity are limited by the inability to accurately account for balance transfers and lack of comprehensive financial data across multiple accounts, leading to incomplete consumer information and inaccurate spending behavior models.
Innovation Solution
A method and apparatus for modeling consumer behavior using tradeline data, consumer panel data, and internal customer data to estimate spending levels, identify balance transfers, and categorize consumers based on spending patterns, enabling more accurate assessment of purchasing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple balance monitoring is used to estimate consumer spend capacity, then the method is easy to implement, but the accuracy of spending behavior estimation deteriorates due to inability to distinguish balance transfers from actual spending
Solution Approach 1:
The patent segments the consumer financial data into multiple account types (credit accounts, checking accounts, savings accounts, investment accounts) and analyzes balance changes within each segment. By examining the pattern of balance changes across different account types and time periods, the system can distinguish between balance transfers and actual spending, thereby improving estimation accuracy while maintaining implementation feasibility through structured data organization.
Solution Approach 2:
The patent performs preliminary analysis of historical balance data to establish baseline spending patterns and identify account relationships before making spending capacity estimates. By pre-processing the data to recognize typical balance transfer scenarios versus actual spending patterns, the system improves the accuracy of subsequent estimations without adding complexity to the core implementation.
2Measurement precision
If comprehensive financial data from multiple accounts is collected to improve spending estimation accuracy, then the precision of consumer behavior modeling improves, but the complexity of data collection and processing increases due to privacy restrictions and data access limitations
Solution Approach 1:
The patent introduces an intermediary analysis layer that processes financial data without requiring direct access to proprietary account details. The system uses aggregated balance information and standardized data formats that can be obtained through existing privacy-compliant channels, thereby reducing data collection complexity while maintaining modeling precision through sophisticated analysis of the available data.
Solution Approach 2:
The patent creates a universal modeling framework that can process multiple types of financial data (credit accounts, checking accounts, savings accounts, investment accounts) using the same analytical approach. This multi-functional system handles diverse data sources through standardized processing methods, reducing the overall complexity of the data collection and analysis system while improving precision through comprehensive data utilization.
3Measurement precision
If balance transfer identification algorithms are applied to distinguish transfers from spending, then the accuracy of spend capacity estimation improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies balance transfer identification algorithms selectively to specific account types and time periods where transfers are most likely to occur, rather than processing all data uniformly. By focusing computational resources on critical segments of the data, the system achieves high accuracy in spend capacity estimation while minimizing overall processing time through targeted analysis.
Solution Approach 2:
The patent performs preliminary filtering and categorization of balance changes to identify likely transfer scenarios before applying complex identification algorithms. By pre-processing the data to separate obvious spending patterns from potential transfers, the system reduces the computational burden on the main algorithm, thereby improving accuracy while reducing processing time.
Data Source
AI summary
Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumer's spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. The likelihood of default determined by the SOW model, when applied to a loan portfolio, can reduce the amount of credit enhancement required for an asset-backed securities rating.