Credit eligibility predictor

A system, comprising:

• a processor;
• a computer readable memory in circuit communication with the processor;
• and
• a computer readable storage medium in circuit communication with the processor;
• and
• wherein the processor executes program instructions stored on the computer-readable storage medium via the computer readable memory and thereby:
• extracts, from payroll data of each of a plurality of employees of an organization, data that is historically associated to previous instances of certified tax credit eligibility;
• normalizes the extracted data with respect to data type and data value;
• generates, via a neural network classifier, from an input of the normalized extracted data, multi-class outputs for each employee that indicate strengths of likelihood that each employee is currently eligible for each of a plurality of different tax credits, wherein the neural network classifier is trained on normalized data values and historic success rates for each of a plurality of persons that applied for the tax credits;
• filters the normalized extracted data by removing portions of the normalized extracted data that are associated to ones of the employees that are indicated within the multi-class outputs as having no likelihood that they are currently eligible for any of the plurality of different tax credits, thereby generating a remainder set of normalized extracted data that is associated to the remainder eligible ones of the employees;
• and
• prioritizes an order of application for each of the tax credits for the remainder eligible ones of the employees as a function of respective values and likelihoods of eligibility of the tax credits indicated within the remainder set of normalized extracted data.

A computer program product, comprising:

• a computer readable storage medium having computer readable program code embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the computer readable program code comprising instructions for execution by a processor that cause the processor to:
• extract, from payroll data of each of plurality of employees of an organization, data that is historically associated to previous instances of certified tax credit eligibility;
• normalize the extracted data with respect to data type and data value;
• generate, via a neural network classifier, from an input of the normalized extracted data, multi-class outputs for each employee that indicate strengths of likelihood that each employee is currently eligible for each of a plurality of different tax credits, wherein the neural network classifier is trained on normalized data values and historic success rates for each of a plurality of persons that applied for the tax credits;
• filter the normalized extracted data by removing portions of the normalized extracted data that are associated to ones of the employees that are indicated within the multi-class outputs as having no likelihood that they are currently eligible for any of the plurality of different tax credits, thereby generating a remainder set of normalized extracted data that is associated to the remainder eligible ones of the employees;
• and
• prioritize an order of application for each of the tax credits for the remainder eligible ones of the employees as a function of respective values and likelihoods of eligibility of the tax credits indicated within the remainder set of normalized extracted data.