Earning Code Classification

Abstract

Managing and applying human resources data comprising aggregating employee transaction data for an organization. A number of human resources-related attributes are evaluated across heterogeneous transaction data. The employee transaction data is classified via statistical machine learning into a number of normalized codes according to the human resources-related attributes, a user interface is presented to adjust a number of organizational operating procedures according to the normalized codes.

What is claimed is: 
     
         1 . A computer-implemented method for classifying and applying human resources data, the method comprising:
 aggregating, by a number of processors, employee transaction data for an organization;   evaluating, by a number of processors, a number of human resources-related attributes across heterogeneous transaction data;   classifying, by a number of processors via statistical machine learning, the employee transaction data into a number of normalized codes according to the human resources-related attributes; and   presenting, by a display, a user interface to adjust a number of organizational operating procedures according to the normalized codes.   
     
     
         2 . The method of  claim 1 , wherein the machine learning further comprises:
 labelling the compensation data by independent first and second labelers according to code descriptions, wherein label agreements between the first and second labelers form a first labeled dataset;   resolving label disagreements between the first and second labelers by a third labeler to form a second labeled dataset;   labelling the compensation data with a semantic matcher to form a third labeled dataset;   combining the first, second, and third labeled datasets into a final labeled dataset; and   applying the final labeled dataset to a number of machine learning algorithms.   
     
     
         3 . The method of  claim 2 , wherein machine learning algorithms comprise at least one of:
 naïve Bayes;   logistic regression;   fully connected neural network;   distributed random forest;   gradient boosting machine; or   XG boost.   
     
     
         4 . The method of  claim 1 , wherein the transaction data is labeled according to code descriptions. 
     
     
         5 . The method of  claim 1 , further comprising deriving, by a number of processors, a number of transaction patterns relative to the normalized codes. 
     
     
         6 . The method of  claim 5 , wherein the transaction patterns comprise at least one of:
 employee ratio;   compensation frequency;   earning amount;   job category;   compensation rate type;   employee seniority;   working hours; and   employee age.   
     
     
         7 . The method of  claim 1 , wherein the normalized codes comprise at one of the following:
 pay codes;   benefits codes;   health care costs; or   deduction codes.   
     
     
         8 . The method of  claim 1 , wherein adjusting organizational operating procedures according to the normalized codes comprises at least one of:
 adjusting resource allocation;   employee compensation setup.   
     
     
         9 . The method of  claim 1 , further comprising benchmarking the organization according to the normalized codes and sector. 
     
     
         10 . A system for classifying and applying human resources data, the system comprising:
 a bus system;   a storage device connected to the bus system, wherein the storage device stores program instructions; and   a number of processors connected to the bus system, wherein the number of processors execute the program instructions to:
 aggregate employee transaction data for an organization; 
 evaluate a number of human resources-related attributes across heterogeneous transaction data; 
 classify, via statistical machine learning, the employee transaction data into a number of normalized codes according to the human resources-related attributes; and 
 present a user interface to adjust a number of organizational operating procedures according to the normalized codes. 
   
     
     
         11 . The system of  claim 10 , wherein the machine learning further comprises:
 labelling the compensation data by independent first and second labelers according to code descriptions, wherein label agreements between the first and second labelers form a first labeled dataset;   resolving label disagreements between the first and second labelers by a third labeler to form a second labeled dataset;   labelling the compensation data with a semantic matcher to form a third labeled dataset;   combining the first, second, and third labeled datasets into a final labeled dataset; and   applying the final labeled dataset to a number of machine learning algorithms.   
     
     
         12 . The system of  claim 11 , wherein machine learning algorithms comprise at least one of:
 naïve Bayes;   logistic regression;   fully connected neural network;   distributed random forest;   gradient boosting machine; or   XG boost.   
     
     
         13 . The system of  claim 10 , wherein the transaction data is labeled according to code descriptions. 
     
     
         14 . The system of  claim 10 , wherein the number of processors further execute program instructions to derive a number of transaction patterns relative to the normalized codes. 
     
     
         15 . The system of  claim 14 , wherein the transaction patterns comprise at least one of:
 employee ratio;   compensation frequency;   earning amount;   job category;   compensation rate type;   employee seniority;   working hours; and   employee age.   
     
     
         16 . The system of  claim 10 , wherein the normalized codes comprise at one of the following:
 pay codes;   benefits codes;   health care costs; or   deduction codes.   
     
     
         17 . The system of  claim 10 , wherein adjusting organizational operating procedures according to the normalized codes comprises at least one of:
 adjusting resource allocation;   employee compensation setup.   
     
     
         18 . The system of  claim 10 , wherein the number of processors further execute program instructions to benchmark the organization according to the normalized codes and sector. 
     
     
         19 . A computer program product for classifying and applying human resources data, the computer program product comprising:
 a non-volatile computer readable storage medium having program instructions embodied therewith, the program instructions executable by a number of processors to cause the computer to perform the steps of:
 aggregating employee transaction data for an organization; 
 evaluating a number of human resources-related attributes across heterogeneous transaction data; 
 classifying, via statistical machine learning, the employee transaction data into a number of normalized codes according to the human resources-related attributes; and 
 presenting a user interface to adjust a number of organizational operating procedures according to the normalized codes. 
   
     
     
         20 . The computer program product according to  claim 19 , wherein the machine learning further comprises:
 labelling the compensation data by independent first and second labelers according to code descriptions, wherein label agreements between the first and second labelers form a first labeled dataset;   resolving label disagreements between the first and second labelers by a third labeler to form a second labeled dataset;   labelling the compensation data with a semantic matcher to form a third labeled dataset;   combining the first, second, and third labeled datasets into a final labeled dataset; and   applying the final labeled dataset to a number of machine learning algorithms.   
     
     
         21 . The computer program product according to  claim 20 , wherein machine learning algorithms comprise at least one of:
 naïve Bayes;   logistic regression;   fully connected neural network;   distributed random forest;   gradient boosting machine; or   XG boost.   
     
     
         22 . The computer program product according to  claim 19 , wherein the transaction data is labeled according to code descriptions. 
     
     
         23 . The computer program product according to  claim 19 , further comprising deriving, by a number of processors, a number of transaction patterns relative to the normalized codes. 
     
     
         24 . The computer program product according to  claim 23 , wherein the transaction patterns comprise at least one of:
 employee ratio;   compensation frequency;   earning amount;   job category;   compensation rate type;   employee seniority;   working hours; and   employee age.   
     
     
         25 . The computer program product according to  claim 19 , wherein the normalized codes comprise at one of the following:
 pay codes;   benefits codes;   health care costs; or   deduction codes.   
     
     
         26 . The computer program product according to  claim 19 , wherein adjusting organizational operating procedures according to the normalized codes comprises at least one of:
 adjusting resource allocation;   employee compensation setup.   
     
     
         27 . The computer program product according to  claim 19 , further comprising benchmarking the organization according to the normalized codes and sector.