Churn Prediction Model in Retail Banking Using Fuzzy C-means Clustering

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 cmeans 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.