Consumer Default Risk Model Using Spending Patterns
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Solution Overview
Problem
Current systems lack the capability to accurately estimate a consumer's risk of default due to limited and incomplete consumer information from credit bureaus, and restricted access to detailed financial data from other institutions, hindering financial institutions' ability to target potential prospects and manage risk effectively.
Innovation Solution
A method that determines a comprehensive consumer default risk value by obtaining consumer credit data, modeling spending patterns, and calculating estimated spend capacity, optionally incorporating internal data to provide a more accurate assessment of risk for credit decisions and business strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing credit bureau systems are used to estimate consumer risk, then the process is simple and fast, but the measurement precision of risk assessment is insufficient due to limited consumer information
Solution Approach 1:
The patent combines multiple data sources including credit bureau data, consumer reported data, and alternative data from non-traditional sources into a unified risk assessment system. This merging of diverse data streams enables more accurate risk measurement while managing complexity through integrated processing
Solution Approach 2:
The patent introduces consumer reported data as an intermediary layer that bridges the gap between limited credit bureau information and the need for comprehensive risk assessment. Consumers actively report financial behaviors that are not captured by traditional credit bureaus, providing additional risk signals
2Measurement precision
If detailed financial information from multiple institutions is accessed to improve risk modeling, then the measurement precision increases, but access is restricted by privacy laws and security concerns
Solution Approach 1:
The patent enables consumers to actively report their own financial data directly to the risk assessment system. This self-service approach allows consumers to share information from multiple institutions voluntarily, bypassing privacy restrictions that would otherwise prevent access to comprehensive financial data
Solution Approach 2:
The patent implements preliminary consumer authorization and data sharing agreements before accessing detailed financial information from multiple institutions. By obtaining consent in advance, the system can legally and securely access comprehensive data needed for precise risk assessment
3Reliability
If comprehensive consumer data is collected from multiple sources, then the reliability of risk assessment improves, but the loss of information increases due to data integration challenges
Solution Approach 1:
The patent segments consumer data into distinct categories (credit bureau data, consumer reported data, alternative data) and processes each segment separately before integration. This segmentation preserves the unique characteristics of each data source while enabling comprehensive risk assessment through structured combination
Solution Approach 2:
The patent transforms diverse data from multiple sources into standardized parameters and metrics that can be consistently processed and compared. By converting different data formats and types into uniform parameters, the system maintains information integrity while enabling reliable risk modeling
Data Source
AI summary
The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.