Adaptive Risk Profile Generation for Small Business Lending
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Solution Overview
Problem
Banks face challenges in effectively assessing the creditworthiness of small businesses, often relying on proprietor credit scores rather than comprehensive business performance metrics, leading to inefficient lending decisions.
Innovation Solution
A method and system that generate a risk profile for small businesses by analyzing user-entered data and usage statistics from business management applications using an adaptively-determined matching algorithm, providing lenders with a probability of loan default, and iteratively adjusting the algorithm to improve correlation with actual lending outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If banks use proprietor credit scores to assess small business creditworthiness, then the assessment process is simple and quick, but the accuracy and reliability of lending decisions deteriorates
Solution Approach 1:
The patent segments the credit assessment into multiple components: proprietor credit score, business financial data, business management application data, and usage statistics. Each segment contributes independently to the overall risk profile, allowing comprehensive evaluation while maintaining processing efficiency through modular data collection and analysis.
Solution Approach 2:
The patent merges proprietor credit scores with business-specific data from management applications, usage statistics, and financial records to create a composite risk profile. This combination integrates personal and business creditworthiness indicators, improving assessment accuracy while maintaining streamlined processing through automated data aggregation.
2Measurement precision
If banks implement comprehensive business assessment systems, then lending decision accuracy improves, but system complexity and implementation difficulty increases
Solution Approach 1:
The patent employs a universal risk profile generation system that processes multiple data types (financial data, usage statistics, management application data) through a single adaptive matching algorithm. This multi-functional approach consolidates various assessment functions into one system, improving accuracy while managing complexity through standardized processing procedures.
Solution Approach 2:
The system implements self-service capabilities through automated data collection from business management applications, automatic generation of risk profiles, and iterative algorithm adjustment based on lending outcomes. This automation reduces manual intervention requirements, improving assessment accuracy while keeping system complexity manageable through self-optimizing mechanisms.
3Ease of operation
If banks use static credit assessment methods, then the assessment process is straightforward, but adaptability to changing business conditions deteriorates
Solution Approach 1:
The patent implements dynamic risk profile generation through an adaptive matching algorithm that continuously learns from lending decisions and outcomes. The system adjusts matching parameters based on accumulated data, enabling real-time adaptation to changing business conditions while maintaining operational simplicity through automated updates and standardized processing flows.
Solution Approach 2:
The system incorporates feedback loops where lending decisions and their outcomes are fed back into the adaptive matching algorithm. This feedback mechanism allows the system to learn from actual performance data and adjust risk assessment criteria accordingly, improving adaptability to changing conditions while maintaining straightforward operation through automated feedback processing.
Data Source
AI summary
The method and system involves instant loan decisions by generating a risk profile of a small business (SMB). The risk profile is generated based on accounting data and other third party business management application (BMA) data of the SMB. In particular, the accounting data and other third party BMA data are retrieved from a BMA (e.g., accounting application, payroll application, tax preparation application, personnel application, etc.) as a software-as-an-service (SaaS) used by the SMB. Specifically, the risk profile represents the likelihood of the SMB to be delinquent and/or to default on a loan. The risk profile is then provided to a lender for making an expedient lending decision with respect to the SMB. In addition, statistics of lenders' lending decisions based on provided risk profiles are analyzed to generate a correlation. Accordingly, the algorithm(s) used to generate the risk profile from the accounting data and other third party BMA data are adjusted to maximize the correlation.