Credit risk handling in telecommunication sector
- Monika Szczerba,
- Andrzej Ciemski
This article presents an application of data mining methods in telecommunication sector. This sector becomes a new area of research for particular problem solving e.g. churn prediction, cross-up selling marketing campaigns, fraud detection, customer segmentation and profiling, data classification, association rules discovery, data clustering, parameter importance analysis etc. Credit risk prediction became a new research domain in pattern recognition area aimed to find the most risky customers. This article is devoted to assessing credit risk from the moment of opening a customer account to the moment of closing an account due to non-payment. Algorithms are used to identify and insolvency of a debtor. Credit scoring is presented in a form of activation models, which are used to predict customers’ debt as well as indicate clients with the highest, medium and smallest credit risk. Practical part of the article is based on the real customer database in a telecommunication company.
- Record ID
- Journal series
- Lecture Notes in Artificial Intelligence
- Issue year
- LNAI 5633
- DOI:10.1007/978-3-642-03067-3_11 Opening in a new tab
- (en) English
- Score (nominal)
- Uniform Resource Identifier
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