Customer Exposure View Aggregation for Risk Assessment
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Solution Overview
Problem
Financial institutions face limitations in gathering detailed customer data for due diligence, leading to incomplete risk assessments during business transactions such as extending credit or underwriting loans, as they typically rely on high-level information.
Innovation Solution
A method that aggregates exposure data and cash flow data associated with a customer and their household, using identifiers to collect and communicate comprehensive data across multiple products and lines of business, providing a detailed view to facilitate better risk evaluation and customer retention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If financial institutions rely on high-level information for due diligence, then the data collection process is simple and quick, but the risk assessment completeness deteriorates
Solution Approach 1:
The patent segments customer data into multiple dimensions including individual customer data, household data, and data across multiple products and lines of business. This segmentation allows comprehensive risk assessment by systematically collecting and analyzing specific data categories rather than relying on generic high-level information.
Solution Approach 2:
The system creates a universal data aggregation framework that collects information across multiple products and lines of business. This multi-functional approach enables the same data infrastructure to serve various due diligence needs across different financial products, improving risk assessment completeness without proportionally increasing complexity.
2Measurement precision
If financial institutions collect detailed customer data across multiple products and lines of business, then the risk assessment accuracy improves, but the data aggregation complexity increases
Solution Approach 1:
The patent implements segmentation by organizing detailed customer data into distinct categories: individual customer information, household-level information, and product-specific data. This structured segmentation enables precise risk measurement while managing aggregation complexity through systematic data organization.
Solution Approach 2:
The system adds dimensional depth to customer views by incorporating household-level data and cross-product information. This multi-dimensional approach enhances risk assessment accuracy by examining customer behavior and financial status from multiple angles rather than a single flat perspective.
3Adaptability or versatility
If financial institutions aggregate household-level data, then customer retention capability improves through tailored products, but the processing complexity increases
Solution Approach 1:
The patent segments data at the household level, allowing financial institutions to analyze and create products tailored to specific household needs and characteristics. This segmentation enables customized product offerings while managing processing complexity through organized data structures.
Solution Approach 2:
The system enables self-service capabilities by providing aggregated household data and insights that allow customers to self-identify their needs and qualify for tailored products. This reduces the processing burden on institutional staff while maintaining high adaptability in product offerings.
Data Source
AI summary
According to one embodiment of the present invention, a method comprises receiving a request to aggregate data associated with a customer. The method determines a first identifier associated with the customer that identifies the customer and a second identifier that identifies a household associated with the customer. Data associated with the second identifier is aggregated and communicated in response to the request.