Business expert systems: the competitive edge
Expert Systems with Applications: An International Journal
Enhancing information systems management with natural language processing techniques
Data & Knowledge Engineering - DKE 40
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Mining product maps for new product development
Expert Systems with Applications: An International Journal
Real-time credit card fraud detection using computational intelligence
Expert Systems with Applications: An International Journal
Association rules applied to credit card fraud detection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using neural networks and data mining techniques for the financial distress prediction model
Expert Systems with Applications: An International Journal
Empirical analysis of support vector machine ensemble classifiers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Conversion methods for symbolic features: A comparison applied to an intrusion detection problem
Expert Systems with Applications: An International Journal
Designing an expert system for fraud detection in private telecommunications networks
Expert Systems with Applications: An International Journal
DSS for computer security incident response applying CBR and collaborative response
Expert Systems with Applications: An International Journal
Support vector machine based multiagent ensemble learning for credit risk evaluation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Business information extraction from semi-structured webpages
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The detection of non-technical losses (NTLs), in most papers, commonly deals with the utilization of the registered consumption for each customer; besides, some researchers used the economic activity, the active/reactive ratio and the contract power. Currently, utility company databases store enormous amounts of information on both installations and customers: consumption, technical information on the measure equipment, documentation, inspections results, commentaries of inspectors, etc. In this paper, an integrated expert system (IES) for the analysis and classification of all the available useful information of the customer is presented. Customer classification identifies the presence of an NTL and the problem type. This IES include several modules: text mining module for analysis of inspector commentaries and extraction of additional information on the customer, data mining module to draw up the rules that determine the customer estimate consumption, and the Rule Based Expert System module to analyze each customer using the results of the text and data mining modules. This IES is used with real data extracted from Endesa company databases. Endesa is the most important power distribution company in Spain, and one of the most significant companies of Europe. This IES is used in the test phase by human experts in the Endesa company. In this phase, the IES is used as a Decision Support System (DSS), as it contains another module which provides a report with additional information about the customer and a summarized result that the inspectors can use to reach a decision.