Consumer Spending Capacity Estimation Using Intermediary Data Aggregation
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Solution Overview
Problem
Current methods for estimating consumer spending behavior are limited by the inability to accurately account for balance transfers and lack of comprehensive financial data access, leading to incomplete understanding of purchasing power.
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
1Measurement precision
If comprehensive financial data from multiple institutions is collected to accurately estimate consumer spending capacity, then measurement precision is improved, but device complexity and difficulty of detecting and measuring increase due to privacy laws and security concerns
Solution Approach 1:
The patent introduces an intermediary system that aggregates and processes financial data from multiple institutions. This intermediary layer enables accurate spending capacity estimation without requiring direct access to sensitive financial data, thereby resolving the contradiction between measurement precision and difficulty of detecting and measuring.
Solution Approach 2:
The patent segments consumer spending capacity estimation into multiple components: credit card spending, debit card spending, check writing, and cash transactions. By breaking down the comprehensive financial picture into separate measurable segments, the system achieves accurate overall estimation while working within the constraints of data accessibility.
2Measurement precision
If balance transfers are identified and excluded from spending calculations, then measurement precision is improved, but device complexity increases due to the need for sophisticated algorithms
Solution Approach 1:
The patent applies preliminary actions by pre-configuring rules and algorithms to automatically identify balance transfers before they contaminate spending calculations. This preliminary identification and exclusion process ensures measurement precision is maintained while the complexity is managed through automated preprocessing rather than complex real-time analysis.
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. In addition to credit card companies, SoW outputs may be useful to companies issuing, for example: private label cards, life insurance, on-line brokerages, mutual funds, car sales/leases, hospitals, and home equity lines of credit or loans. “Best customer” models can correlate SoW outputs with various customer groups. A SoW score focusing on a consumer's spending capacity can be used in the same manner as a credit bureau score.