Automatic data segmentation system

Abstract

Aspects include a system and method of automatic data segmentation to optimize a client's collection efforts against individuals serviced by the client. At least accounts receivables data, historical payment data, and credit related data associated with an individual may be provided to a model as input data to predict a recovery value for the individual. The recovery value may be a weighted average of a unit yield and recovery rate. Based on the predicted recovery value and client-provided segmentation boundaries that define segments as a range of recovery values, the individual may be assigned to a segment. The segment may inform the client of a particular collection strategy for the individual to optimize collection efforts. Additionally, recovery values for the individuals serviced by the client may be provided to a comparison system and utilized to directly compare collection efforts across a plurality of clients nationally and/or demographically.

We claim: 
     
         1 . A system for automatic data segmentation, the system comprising:
 a processing unit; and   a memory coupled to the processing unit, the memory storing instructions that, when executed by the processing unit, cause the system to:
 receive, from a plurality of data sources, input data associated with an individual whom a client has provided a service, the input data including one or more of accounts receivable data, payment history data, and credit related data associated with the individual; 
 process the input data using a model to predict a recovery value for the individual; 
 assign the individual to a segment based on the predicted recovery value and boundary definitions for a plurality of segments received from the client, the boundary definitions including a range of recovery values for each segment; and 
 provide the segment to the client, wherein the segment informs the client of a collection strategy for the individual. 
   
     
     
         2 . The system of  claim 1 , wherein the system is further caused to:
 receive, from the client, historical data associated with individuals whom the client provided a service, the historical data including at least input data and a recovery value associated with each individual; and   train the model with the historical data.   
     
     
         3 . The system of  claim 2 , wherein the model is a hyper-dimensional model that includes a dimension for each variable of the historical data. 
     
     
         4 . The system of  claim 3 , wherein each individual from the historical data is represented as a data point corresponding to a value for each variable in the hyper-dimensional model. 
     
     
         5 . The system of  claim 3 , wherein spline interpolation is performed within each dimension of the model. 
     
     
         6 . The system of  claim 2 , wherein the system is further caused to:
 perform regression analysis on the model to determine a relationship between the input data and the recovery value; and   generate a formula based on the determined relationship.   
     
     
         7 . The system of  claim 6 , wherein, to process the input data using the model to predict the recovery value for the individual, the system is further caused to:
 provide the input data as input to the formula to receive the predicted recovery value as output.   
     
     
         8 . The system of  claim 1 , wherein the system is further caused to:
 in response to receiving an actual recovery value for the individual, update the model based on the actual recovery value.   
     
     
         9 . The system of  claim 1 , wherein the system is further caused to:
 provide segmentation data of the client to a comparison system communicatively coupled to the system, the segmentation data including at least the boundary definitions and recovery values for individuals serviced by the client, wherein the segmentation data is used by the comparison system to compare collection efforts of the client to collection efforts across a plurality of clients.   
     
     
         10 . The system of  claim 1 , wherein the recovery value is a weighted average of a unit yield and a recovery rate, the unit yield is a monetary amount received from the individual for the service, and the recovery rate is a ratio of the monetary amount received from the individual to a total monetary amount due for the service. 
     
     
         11 . The system of  claim 1 , wherein the accounts receivable data includes one or more of a total amount owed for the service, an amount owed by a guarantor, an amount owed by the individual, a remaining balance owed by the individual, and any payments. 
     
     
         12 . The system of  claim 1 , wherein the payment history data includes one or more of invoices created for the individual over a predetermined time period, payments received from the individual for the invoices, a time gap between creation of the invoices and receipt of the payments, unpaid invoices, and time delays associated with the unpaid invoices. 
     
     
         13 . The system of  claim 1 , wherein the credit related data includes one or more of a credit score, credit report data, and a healthcare-specific credit score of the individual. 
     
     
         14 . A data segmentation method, comprising:
 receiving, from a plurality of data sources, input data associated with an individual whom a client has provided a service, the input data including one or more of accounts receivable data, payment history data, and credit related data associated with the individual;   processing the input data using a model to predict a recovery value for the individual;   assigning the individual to a segment based on the predicted recovery value and boundary definitions for a plurality of segments received from the client, the boundary definitions including a range of recovery values for each segment; and   providing the segment to the client, wherein the segment informs the client of a collection strategy for the individual.   
     
     
         15 . The method of  claim 14 , further comprising:
 training the model with historical data associated with individuals whom the client provided a service, the historical data including at least input data and a recovery value associated with each individual;   performing regression analysis on the model to determine a relationship between the input data and the recovery value; and   generating a formula based on the determined relationship, wherein to process the input data using the model to predict the recovery value for the individual, the input data is provided as input to the formula and the predicted recovery value is received as output.   
     
     
         16 . The method of  claim 14 , further comprising:
 determining the collection strategy based on the segment; and   providing the collection strategy to the client along with the segment.   
     
     
         17 . The method of  claim 14 , further comprising:
 providing segmentation data of the client to a comparison system, the segmentation data including at least the boundary definitions and recovery values for individuals serviced by the client, wherein the segmentation data is used by the comparison system to compare collection efforts of the client to collection efforts across a plurality of clients.   
     
     
         18 . A comparison system comprising:
 a processing unit; and   a memory coupled to the processing unit, the memory storing instructions that, when executed by the processing unit, cause the system to:
 receive and store segmentation data for a plurality of clients, the segmentation data for each client including at least boundary definitions for a plurality of segments and recovery values of individuals serviced; 
 receive a request to compare collection efforts of a given client to collection efforts across one or more of the plurality of clients; 
 aggregate recovery values for the one or more of the plurality of clients; 
 apply boundary definitions of the given client to the aggregated recovery values; 
 for each segment, determine an average aggregated recovery value across the plurality of clients; 
 for each segment, determine an average recovery value of the given client; 
 for each segment, compare the average recovery value of the given client to the average aggregated recovery value across the plurality of clients; and 
 provide comparison results to the given client. 
   
     
     
         19 . The comparison system of  claim 18 , wherein the request to compare collection efforts of the given client to collection efforts across the one or more of the plurality of clients includes one or more of:
 a request to compare across an entirety of clients; and   a request to compare across a subset of clients having similar demographic characteristics to the given client.   
     
     
         20 . The comparison system of  claim 19 , wherein the comparison system is further caused to receive and store demographic data associated with each of the plurality of clients to enable the comparison across the subset of clients.