Dynamic origination of capital pricing based on historical point-of-sale data

A system for dynamic origination of capital, the system comprising:

• a logistic regression engine, configured to perform a binary logistic regression on subscriber data and metrics derived from historical POS data corresponding to subscribers of a restaurant point-of-sale (POS) subscription service to generate logistic regression coefficients that are used to calculate probability of default (PD) values for each of said subscribers, and configured to employ new subscriber data and metrics to generate updated PD values for said each of said subscribers;
• a revenue forecaster, coupled to said rate processor, configured to employ said historical POS data to predict future POS data for restaurants corresponding to said each of said subscribers, and to employ said future POS data to generate predicted total revenues corresponding to said each of said subscribers over a payback period, and configured to employ new POS data to generate updated predicted total revenues corresponding to said each of said subscribers;
• and
• an offer processor, configured to generate and transmit capital product offers corresponding to said each of said subscribers, wherein said capital product offers comprise a payback period, prices, and maximum dollar amounts that are a percentage of said predicted total revenues, and configured to compare said updated predicted total revenues with said predicted total revenues and said updated PD values with said PD values, and configured to dynamically adjust prices and said maximum dollar amounts according to said updated PDs and said updated predicted total revenues.

A method for dynamic origination of capital, the method comprising:

• performing 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 employing new subscriber data and metrics to generate updated PD values for the each of the subscribers;
• employing the historical POS data to predict future POS data for establishments corresponding to the each of the subscribers, and using the future POS data to generate predicted total revenues corresponding to the each of the subscribers over a payback period, and employing new POS data to generate updated predicted total revenues corresponding to the each of the subscribers;
• and
• generating and transmitting capital product offers corresponding to the each of the subscribers, wherein the MCA offers comprise the payback period, prices, and maximum dollar amounts that are a percentage of the predicted total revenues, and comparing the updated predicted total revenues with the predicted total revenues and the updated PD values with the PD values, and dynamically adjusting the prices and the maximum dollar amounts according to the updated PDs and the updated predicted total revenues.