Lender Risk Assessment Using Consumer Spending Data
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Solution Overview
Problem
Current methods for assessing risk levels associated with lenders and predicting consumer spending capacity are inadequate, leading to inefficiencies in risk management and revenue generation for financial institutions.
Innovation Solution
A system and method that calculates lender default risk factors by analyzing consumer default risk scores and spending patterns, using a combination of internal and credit bureau data to rank lenders and predict consumer spend, thereby enhancing risk assessment and revenue opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive consumer data analysis is performed to improve risk assessment accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the risk assessment process into distinct components: consumer-level risk factors, lender-level risk factors, and interaction effects. This segmentation allows the system to process complex data through modular analysis stages, improving measurement precision while managing system complexity through structured decomposition of the assessment workflow
Solution Approach 2:
The patent creates a composite risk assessment model that combines multiple data types (consumer credit data, lender performance data, interaction data) and multiple risk factors into a unified lender-default-risk-score. This composite approach integrates diverse information sources to achieve comprehensive risk measurement while providing a single integrated output that manages complexity
2Measurement precision
If detailed consumer spending pattern analysis is conducted to improve spend capacity prediction, then measurement precision improves, but loss of time increases
Solution Approach 1:
The patent performs preliminary analysis by pre-calculating consumer-level risk factors and spending patterns, and lender-level risk factors before the actual risk assessment. This preliminary processing organizes and pre-processes data in advance, reducing the time required for final risk calculation while maintaining comprehensive analysis depth for accurate spend capacity prediction
Solution Approach 2:
The patent transforms detailed consumer spending pattern data into aggregated lender-level parameters and risk factors. By changing the parameters from individual consumer details to consolidated lender metrics, the system reduces processing time while preserving the essential information needed for accurate spend capacity prediction
3Reliability
If multiple data sources are integrated to improve risk model comprehensiveness, then reliability improves, but device complexity increases
Solution Approach 1:
The patent creates a universal risk assessment framework that handles multiple data sources (consumer credit data, lender performance data, interaction data) through a single integrated process. The system uses a unified lender-default-risk-score calculation that works across different data types and sources, improving reliability through comprehensive data integration while managing complexity through a universal assessment methodology
Data Source
AI summary
The present invention generally relates to financial data processing, and in particular it relates to lender credit scoring, lender profiling, lender behavior analysis and modeling. More specifically, it relates to rating lenders based on data derived from their respective consumers. Also, the present invention relates to rating consumer lenders based on the predicted spend capacity of their consumers.