Lender Risk Assessment via Consumer Spending Segmentation
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Solution Overview
Problem
Current methods for assessing and managing risk levels associated with lenders and predicting consumer spending capacity are inadequate, leading to inefficiencies in financial institutions' risk management and revenue generation.
Innovation Solution
A system and method for lender credit scoring, profiling, and behavior analysis that involves calculating lender default risk factors and consumer spending patterns using comprehensive consumer default risk values and Size of Wallet (SoW) scores, based on internal and external data, to rank lenders and predict account defaults and consumer spending.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional risk assessment methods are used, then the process is simple, but the accuracy of risk assessment and prediction is insufficient
Solution Approach 1:
The system segments risk assessment into multiple components: consumer-level risk factors, lender-level risk factors, and interaction effects. It divides the prediction model into separate modules for consumer default risk, lender default risk, and their combined impact, allowing each component to be optimized independently while improving overall accuracy.
Solution Approach 2:
The patent implements a nested prediction structure where consumer-level risk assessments are nested within lender-level risk frameworks. The consumer default risk factors are evaluated first, then aggregated to assess lender-level risk, with the nested structure allowing hierarchical optimization of risk assessment at different levels.
2Measurement precision
If comprehensive consumer data is collected and analyzed, then the prediction accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing consumer default risk factors, lender default risk factors, and their interaction terms before actual predictions are needed. This pre-processing of risk assessments allows for faster prediction during actual use without requiring re-computation of all consumer data.
Solution Approach 2:
The patent transforms comprehensive raw consumer data into summarized risk parameters and scores. By converting detailed consumer credit data, spending patterns, and lender information into condensed risk factors and interaction terms, the system reduces the computational burden while maintaining prediction accuracy.
3Reliability
If lender risk factors are considered in addition to consumer factors, then the comprehensive risk assessment improves, but the model complexity increases
Solution Approach 1:
The interaction effect model serves multiple functions simultaneously: it captures consumer-lender risk interactions, predicts account default probability, and provides lender-level risk assessment. This multi-functionality allows a single model structure to handle multiple risk assessment needs without proportionally increasing complexity.
Data Source
AI summary
The present disclosure 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 disclosure relates to rating consumer lenders based on the predicted spend capacity of their consumers.