T. Ono, H. Yoshikawa, M. Morita, and N. Komoda (Japan)
Banking applications, predictions, financial behavior features, data mining, and feature selection
This paper proposes a method of predicting which customers’ account balances will increase by using data mining to effectively and efficiently promote sales. Prediction by mining all the data in a business is difficult because of much time required to collect, process, and calculate it. Which features are selected for prediction is critical. We propose a method of selecting features to improve the accuracy of prediction within practical time limits. It consists of three parts: (1) converting collected features into financial behavior features that reflect customer actions, (2) extracting features affecting increases in account balances from these collected and financial behavior features, and (3) predicting customers whose account balances will increase based on the extracted features. We found the accuracy of prediction in an experiment with our method to be higher than with a method that did not use financial behavior features and a method that used features selected by decision trees.
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