Lender Risk Scoring via Consumer Spending Pattern Analysis
Find Innovative SolutionsGenerate Solutions
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 for lender credit scoring and consumer behavior analysis that involves calculating lender default risk factors and consumer spending patterns using comprehensive risk values derived from internal and credit bureau data, enabling ranking of lenders and prediction of account defaults and consumer spending.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive consumer data from multiple lenders is collected and analyzed, then the precision of risk assessment and spending prediction improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the risk assessment system into distinct components: consumer data collection modules, lender-specific data processing units, risk factor calculation engines, and spending pattern analysis systems. Each segment handles specific tasks independently, allowing the overall system to achieve high precision through comprehensive data analysis while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary elements such as risk factor weights, spending capacity metrics, and lender association indices that mediate between raw consumer data and final risk assessments. These intermediaries transform complex multi-lender data into standardized measurable parameters, improving precision while reducing system complexity through abstraction.
2Measurement precision
If detailed consumer spending patterns and lender associations are analyzed, then the accuracy of spending capacity prediction improves, but the amount of data processing required increases
Solution Approach 1:
The patent implements preliminary actions by pre-calculating and storing risk factors, spending patterns, and lender associations in structured databases before actual risk assessment queries. Consumer data is pre-processed into standardized formats with pre-computed metrics, allowing rapid retrieval and analysis during prediction tasks, thereby improving accuracy without proportionally increasing processing time.
Solution Approach 2:
The patent transforms detailed consumer behavior data into condensed parameters such as risk factor weights, spending capacity indices, and lender association scores. By changing the parameter representation from raw transactional data to aggregated statistical measures, the system achieves high prediction accuracy while reducing the computational burden of data processing.
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.