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Artificial intelligence trending system
| Details |
Inventors: Tayebnejad, Mohammad Reza; Van Camp, Karl Aric; Dallas, Charles Alan; Van Arkel, John Hans;
Assignee: MCI, LLC (Basking Ridge, NJ)
Primary Examiner: Hirl; Joseph P.
Assistant Examiner:
Attorney, Agent or Firm:
A data processing system program to develop, train, and implement a neural network for identifying customers who represent a bad debt risk is disclosed. A feature vector is applied to a neural network to generate outputs that approximate the relative likelihood that customers who are the subjects of the records used to generate the feature vector will be a bad debt risk. Statistical values relating categorical attributes of the customers to the likelihood of their becoming a bad debt risk are substituted for the categorical attributes, and the attributes are normalized before the feature vector is applied to the network. In one embodiment the customers are customers of a long distance service provider. |
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DETAILED DESCRIPTION These and other needs are addressed by the present invention by means of a method and system, and computer-readable media carrying instructions for controlling the system to carry out the method. In accordance with an embodiment of the present invention a rank order for records with respect to a selected characteristic of subjects of the records is estimated. The records include attributes of the subjects, and the attributes generally include at least one, and typically more than one, categorical attribute. By "categorical attribute" herein is meant a representation of discrete classes or categories which, in its raw form, has no mathematical significance. The numeric value or label of a categorical attribute serves only to distinguish among various categorical attributes. For example, the area code 540 associates the subject with a particular geographic area within Virginia, but does not indicate five hundred and forty of anything; and the sum, difference, or average of two or more area codes is not meaningful. The system is programmed to derive any derived categorical attributes used and carry out the steps of: a) estimating statistics relating values of the categorical attributes to the characteristic; b) for one of the records, applying a feature vector having elements derived from the attributes in the one record, the elements including values of the statistics corresponding to a values of the categorical attributes in the one record, to an artificial intelligence algorithm, which is preferably a trained neural network, to obtain an output value; c) repeating step b for each remaining one of the records; and d) rank ordering the records in accordance with the output values. In accordance with an aspect of the invention the subjects are customers of a provider of goods or services and the characteristic is potential to be a bad debt risk. In accordance with another aspect of the invention the provider is a provider of long distance services, and the attributes include attributes derived from current traffic data
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