Consumer Spend Capacity Estimation Using Segmented Data Analysis
Find Innovative SolutionsGenerate Solutions
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 access, leading to incomplete consumer information and inaccurate spending ability assessments.
Innovation Solution
A method and apparatus for modeling consumer behavior using tradeline data, consumer panel data, and internal customer data to determine spend patterns, identify balance transfers, and calculate the size of wallet, enabling more accurate spending level estimations and customer categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive financial data from multiple institutions is collected to improve spend capacity estimation accuracy, then measurement precision improves, but device complexity and difficulty of detecting and measuring increase due to privacy laws and security restrictions
Solution Approach 1:
The patent segments the data collection process into multiple components: internal account data, credit bureau data, and consumer panel data from third parties. Each data source is processed separately through dedicated modules that apply specific algorithms appropriate to each source type, allowing the system to manage complexity while achieving comprehensive coverage
Solution Approach 2:
The patent employs intermediaries such as credit bureaus and consumer panel providers who act as mediators between the financial institution and other financial institutions. These intermediaries aggregate and process data from multiple sources, reducing the direct complexity of inter-institutional data collection while maintaining accuracy through their specialized processing capabilities
2Measurement precision
If balance transfers are identified and excluded from spending calculations to improve accuracy, then measurement precision improves, but difficulty of detecting and measuring increases due to the subtle nature of balance transfer patterns
Solution Approach 1:
The patent applies dynamic analysis to balance detection by examining balance patterns over time rather than static snapshots. The system monitors balance changes across multiple periods, identifying characteristic patterns of balance transfers versus genuine spending, and adjusts detection thresholds based on learned consumer behavior patterns
Solution Approach 2:
The system incorporates feedback mechanisms where detected balance transfers and spending patterns are fed back into the model to refine future detections. The algorithm learns from confirmed balance transfer cases and adjusts its detection parameters, improving accuracy over time while reducing false positives
3Measurement precision
If detailed analysis of full range of financial accounts is performed to improve spend capacity estimation, then measurement precision improves, but loss of time increases due to the extensive data processing required
Solution Approach 1:
The patent performs preliminary actions by pre-processing and aggregating data from credit bureaus and consumer panels before the actual spend capacity analysis. Historical balance data is pre-categorized and indexed, allowing the system to quickly retrieve and analyze relevant information without processing entire datasets during the estimation process
Solution Approach 2:
The system applies partial action by focusing analysis on the most relevant data elements for spend capacity estimation. Rather than processing every detail of all financial accounts equally, the algorithm identifies and prioritizes key indicators such as balance trends, payment patterns, and credit utilization, processing only the necessary subset of data required for accurate estimation
Data Source
AI summary
Time series consumer spending data, point-in-time balance information, internal customer financial data and consumer panel information provides input to a model for consumer spend behavior on plastic instruments or other financial accounts, from which approximations of spending ability may be reliably identified and utilized to promote additional consumer spending.