Composite Consumer Risk Model Using Multi-Source Data
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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 for determining a comprehensive consumer default risk value by obtaining consumer credit data, modeling spending patterns, and calculating an estimated spend capacity, which may also incorporate internal data to provide a more accurate assessment of risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing credit bureau systems are used to estimate consumer risk, then the process is simple and data access is easy, but the consumer information is limited and incomplete
Solution Approach 1:
The patent combines multiple data sources including credit bureau data, internal financial institution data, and alternative data sources into a unified consumer risk assessment model. This merging of previously separate information streams resolves the contradiction by providing more complete consumer information while managing system complexity through integrated architecture.
Solution Approach 2:
The risk assessment system is designed to universally accept and process multiple types of data from various sources (credit bureaus, internal systems, alternative sources). This multi-functional capability allows the system to overcome information limitations without requiring separate specialized systems for each data source.
2Measurement precision
If detailed financial information from multiple institutions is accessed to improve risk assessment accuracy, then consumer risk evaluation becomes more accurate, but access is restricted by privacy laws and security concerns
Solution Approach 1:
The patent introduces a centralized risk assessment platform that acts as an intermediary between multiple financial institutions and consumers. This mediator collects, standardizes, and processes data from various sources while managing privacy and security requirements, enabling accurate risk assessment without direct institution-to-institution data sharing restrictions.
Solution Approach 2:
The system transforms raw financial data from multiple sources into standardized risk parameters and consumer profiles. By changing the parameter representation from raw institutional data to standardized risk metrics, the system achieves accurate cross-institutional comparison while navigating privacy and security constraints.
3Reliability
If traditional credit scoring models are used, then the model is simple to implement, but it cannot accurately estimate consumer risk of default
Solution Approach 1:
The patent creates a composite risk assessment model that integrates traditional credit scoring factors with alternative data sources and internal financial institution data. This composite approach combines the reliability of established methods with the informational depth of newer data sources, achieving superior default risk estimation while managing model complexity through structured integration.
Solution Approach 2:
The risk assessment model is segmented into multiple components: traditional credit bureau data analysis, internal data processing, alternative data integration, and synthetic profile generation. This segmentation allows each component to be optimized independently while maintaining overall model reliability and managing complexity through modular architecture.
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.