Consumer Spend Capacity Modeling Using Credit Bureau Data
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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 institutions, 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 consumer spend capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple monitoring of credit account balances is used to estimate consumer spend capacity, then the method is easy to implement, but the accuracy of spending behavior estimation is poor due to inability to distinguish balance transfers from actual spending
Solution Approach 1:
The patent introduces an intermediary analysis layer that processes credit bureau data to identify balance transfer patterns. This intermediary system distinguishes between balance transfers and actual spending by analyzing transaction characteristics, thereby improving estimation accuracy without requiring direct access to all financial accounts.
Solution Approach 2:
The patent creates a modeled representation of consumer spending behavior based on available credit data. By developing algorithms that replicate actual spending patterns from credit bureau records, the system achieves accurate spending estimation using only the data already available, without needing additional account access.
2Measurement precision
If comprehensive examination of all consumer financial accounts across multiple institutions is conducted to achieve complete picture of purchasing ability, then the accuracy of spend capacity estimation is improved, but access is restricted by consumer privacy laws and security concerns
Solution Approach 1:
The patent develops a universal modeling system that works with the limited data type that is universally available - credit bureau data. The algorithms are designed to extract maximum spending behavior information from this single data source, making the solution applicable to all consumers regardless of which financial institutions they use.
Solution Approach 2:
The patent creates accurate copies of consumer spending behavior patterns using only credit bureau data. By developing algorithms that replicate actual spending from this limited data source, the system achieves comprehensive spend capacity assessment without needing to access restricted account information across multiple institutions.
3Adaptability or versatility
If credit bureau data is used as the primary data source, then consumer privacy requirements are met and data access is feasible, but the completeness of consumer financial information is insufficient to accurately identify spending behavior
Solution Approach 1:
The patent creates accurate representations of complete spending behavior by analyzing patterns in credit bureau data. The algorithms reconstruct full spending profiles by identifying balance transfer patterns and distinguishing them from actual spending, effectively copying the information that would be present in complete account data using only the available credit bureau records.
Solution Approach 2:
The patent transforms the parameters of the available data by changing how credit bureau information is analyzed and interpreted. By applying sophisticated algorithms that detect balance transfer patterns and spending behaviors, the system extracts maximum information from the existing data parameters, effectively compensating for the incomplete nature of the source data.
Data Source
AI summary
Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumers 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 outputs can be used as attributes to consider in developing a credit bureau scorecard.