Churn Prediction Model in Retail Banking Using Fuzzy C-Means Clustering
published: Nov. 7, 2008, recorded: October 2008, views: 7579
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The paper presents model based on fuzzy methods for churn prediction in retail banking. The study was done on the real, anonymised data of 5000 clients of a retail bank. Real data are great strength of the study, as a lot of studies often use old, irrelevant or artificial data. Canonical discriminant analysis was applied to reveal variables that provide maximal separation between clusters of churners and non-churners. Combination of standard deviation, canonical discriminant analysis and k-means clustering results were used for outliers detection.
Due to the fuzzy nature of practical customer relationship management problems it was expected, and shown, that fuzzy methods performed better than the classical ones. According to the results of the preliminary data exploration and fuzzy clustering with different values of the input parameters for fuzzy c-means algorithm, the best parameter combination was chosen and applied to training data set. Four different prediction models, called prediction engines, have been developed. The definitions of clients in the fuzzy transitional conditions and the distance of k instances fuzzy sums were introduced.
The prediction engine using these sums performed best in churn prediction, applied to both balanced and non-balanced test sets.
Download slides: sikdd08_popovic_cpm_01.pdf (1.2 MB)
Download slides: sikdd08_popovic_cpm_01.ppt (1.7 MB)
Download article: sikdd08_popovic_cpm_article.pdf (495.9 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !