Selecting Variables For A Treatment Optimization Model

A computer implemented method for selecting variables for a treatment optimization model to determine an optimal treatment from a number of treatments for a financial account, the method comprising:

• receiving data representing a plurality of variables associated with the financial account;
• storing each one of a plurality of variables in at least one storage bin defined in a database, such that each variable has at least N samples;
• determining a measure of informativity (inf) for each one of the plurality of variables, the measure of informativity defining how good each variable is for making predictions;
• determining a measure of usefulness (Mu) for each one of the plurality of variables, the measure of usefulness defining how good each variable is for making predictions of differences in an outcome of each of the number of treatments;
• determining a set of best variables from each variable in each storage bin based on inf and Mu;
• generating the optimal treatment based on the set of best variables.

A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to implement, when executed by a computer processor, a method for selecting variables for a treatment optimization model to determine an optimal treatment from a number of treatments for a financial account, the computer readable program code comprising instructions for the computer processor to:

• receive data representing a plurality of variables associated with the financial account;
• store each one of a plurality of variables in at least one storage bin defined in a database, such that each variable has at least N samples;
• determine a measure of informativity (inf) for each one of the plurality of variables, the measure of informativity defining how good each variable is for making predictions;
• determine a measure of usefulness (Mu) for each one of the plurality of variables, the measure of usefulness defining how good each variable is for making predictions of differences in an outcome of each of the number of treatments;
• select a set of best variables from each variable in each storage bin based on inf and Mu;
• and
• generate the optimal treatment based on the set of best variables.