Automated Capital Origination Using POS Data for SMB Risk Assessment
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Solution Overview
Problem
Small-to-medium business owners, particularly in the hospitality industry, face difficulties in obtaining capital due to the time-consuming and cumbersome conventional loan application processes, which often require extensive financial documentation and do not account for industry-specific or seasonal fluctuations in revenue.
Innovation Solution
A system for automated capital origination that uses historical point-of-sale data to calculate probability of default and predict future revenues, allowing for dynamic pricing and automatic capital product offers and payment collection, tailored to the business's specific geographic and seasonal characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional loan application processes are used with extensive financial documentation requirements, then lenders can assess risk accurately using traditional metrics, but the process becomes time-consuming and burdensome for small-to-medium business owners
Solution Approach 1:
The patent replaces the manual mechanical process of collecting and reviewing extensive financial documentation with an automated electronic system that uses machine learning algorithms to analyze point-of-sale data, tax information, and business transactions. This substitution dramatically reduces the time required for risk assessment while maintaining or improving accuracy through consistent algorithmic evaluation.
Solution Approach 2:
The system enables businesses to automatically provide required financial information through integrated point-of-sale systems and tax software connections. The automated platform collects, organizes, and submits necessary data without requiring business owners to manually prepare documentation, thereby reducing the time and effort needed for the capital origination process.
2Device complexity
If traditional one-size-fits-all lending criteria are applied across all industries, then lenders can simplify their underwriting process, but they fail to account for industry-specific and seasonal fluctuations in revenue
Solution Approach 1:
The patent implements dynamic underwriting criteria that automatically adjust based on the borrower's industry, seasonality patterns, and historical performance. The machine learning models are trained to recognize industry-specific revenue patterns and seasonal fluctuations, allowing the system to evaluate each business according to its unique characteristics rather than applying static, uniform standards.
Solution Approach 2:
The system segments businesses by industry type, geographic location, and operational characteristics to apply tailored evaluation criteria. This segmentation allows the platform to account for industry-specific revenue patterns and seasonal variations while maintaining a streamlined automated process, rather than treating all businesses identically.
3Quantity of substance
If SMB owners spend time generating and providing extensive financial documentation, then they can obtain capital through conventional channels, but this time away from hands-on management results in decreased profits
Solution Approach 1:
The patent replaces the manual process of preparing and submitting financial documentation with an automated electronic system that directly accesses point-of-sale data, tax information, and business transactions. This substitution eliminates the need for business owners to spend time on administrative tasks while maintaining access to capital through the automated evaluation process.
Solution Approach 2:
The automated platform serves as an intermediary that directly connects businesses with capital providers through electronic data exchange. The system acts as a mediator that automatically evaluates risk and facilitates funding without requiring business owners to engage in time-consuming documentation preparation, thereby protecting their management productivity.
Data Source
AI summary
A system for automated origination of capital client engagement is provided. The system includes a rate processor, a revenue forecaster, and an offer processor. The rate processor is configured to perform a binary logistic regression on subscriber data and metrics derived from historical POS data corresponding to subscribers of a point-of-sale (POS) subscription service to generate logistic regression coefficients that are used to calculate probability of default (PD) values for each of the subscribers, and configured to employ new subscriber data and metrics to generate updated PD values for the each of the subscribers. The revenue forecaster is coupled to the rate processor and is configured to employ the historical POS data to predict future POS data for establishments corresponding to the each of the subscribers, and to employ the future POS data to generate predicted total revenues corresponding to the each of the subscribers over a payback period, and configured to employ new POS data to generate updated predicted total revenues corresponding to the each of the subscribers. The offer processor is configured to compare the updated predicted total revenues with the predicted total revenues and the updated PD values with the PD values, and configured to automatically generate and transmit engagement instructions for establishments that have received a capital product offer and whose updated PD has increased by more than a threshold amount over the PD.