Consumer Spend Capacity Modeling 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 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 assessments of consumer spend capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional credit scoring methods are used, then credit risk assessment is provided, but spend capacity estimation is inaccurate
Solution Approach 1:
The patent combines multiple data sources including tradeline data, consumer panel data, and internal customer data into a unified consumer behavior model. This merging of previously separate information streams enables comprehensive spend capacity estimation that overcomes the limitations of traditional credit scoring which relied on incomplete data sources.
Solution Approach 2:
The patent segments consumer behavior into distinct components by identifying and separating balance transfers from actual spending activities. This segmentation allows the system to accurately distinguish between balance reductions due to transfers (which don't represent spending) and balance reductions due to actual purchases, thereby improving spend capacity measurement precision.
2Measurement precision
If balance data is simply monitored, then account balance changes are tracked, but balance transfers cannot be distinguished from spending
Solution Approach 1:
The patent applies preliminary processing to tradeline data to identify patterns characteristic of balance transfers before final spend capacity calculation. By pre-identifying and flagging balance transfer transactions through analysis of trading partner information and transaction patterns, the system separates transfer activity from spending activity early in the processing chain, improving measurement precision without requiring complex real-time analysis.
3Loss of information
If comprehensive financial account examination is performed, then complete purchasing ability picture is achieved, but consumer privacy laws and security concerns restrict access
Solution Approach 1:
The patent uses consumer panel data as an intermediary source that provides comprehensive financial behavior information without requiring direct access to consumers' private financial accounts. This intermediary data source, combined with publicly available tradeline data and internal customer data, enables the system to construct a complete picture of purchasing ability while respecting privacy constraints and security concerns.
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. A SOW score focusing on a consumer's spending capability can be used in the same manner as a credit bureau score.