Credit Analysis Of Power Clients And Default Forewarning In Electricity Market

With deeper reform in electricity industry, electric power companies have to face with the problem, i.e. great amount of account receivable is dued by clients. In order to ensure electric power enterprise have reasonable cash flow as well as gain corresponding profit, it is necessary to enhance clients'credit management.First of all, considering that electric power company is at disadvantage situation on the issue of electricity fees payment, we design a credit assessment systems which are suitable for electric power company. The data used in evaluating is conveniencely collected from MIS (managemant information system) or from public bulletin. In addition, to measure the difference of clients'credit, we mined the database of payment record, and design a new algorithm which is able to dynamically calculate their credit. The proposed algorithm could distinguish complicated paying behaviors objectively. For example, whether electrivity fee is paid on time or not, is in debt or not, if accumulative arrears existed for long time or short time etc. Considering that lots of clients are needed to be evaluated monthly, in order to save the management cost and make the investigation being accessible, we design a survey questionnair, which is assigned to staffs who are responsible for meter-reading. Therefore, we got credit information efficiencelly by our staffs in the electric power company.Then, with principal component approach and catastrophe theory, we accomplished the comprehensive credit evaluation for the purpose of subdivision clients and classification management. By means of credit risk analysis technique, we trained historical data and constructed credit discriminent models based on Beyes, ANN, RBF, ANFIS etc., then we could use the lastest credit information to judge the clients claasification. In the case we studied,correct-judging rate of the model achieved to 85% above. If the logistic discriminent model is employed, to estimate the default probability is available.Finally, we analyzed the reasons of arrears, designed default forewarning indecies and defined corresponding forewarning range. The indecies are simple, observable, and also adjustable according to the management requirement. Compared to the forewarning benchmark,it is easily for us to know if the client must be listed in the warning sheet. Besides, with grey system theory, we constructed a model which is able to forecast the credit variation of a client. With the determined rules, then we got to know if the client would likely enter the forewarning range. These results from model forewarning and statistical forewarning are an important reference in electricity fee management. We also pointed out that multimal control means in controlling default risk are very necessary, and analyzed the validity of pre-paying management system.