Prediction of Bank Customer Response to a Personal Loan Campaign

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Abstract Marketing campaigns are a very critical decision for a business that can be unsuccessful if the product or service delivered does not attract interested customers, thus the key objective for direct marketing prediction is to identify potential customers from an existing database so that marketers can design accurate strategies to increase sales and profitability. Machine learning methods have been successfully used in direct marketing to predict customer response in the banking industry. In this project, the logistic regression, random forest, and neural network algorithms are used to predict the response of bank customers to a personal loan campaign according to their demographic information and transactional patterns. Moreover, detecting the features interaction terms and examining their influence on the performance of the logistic regression model. The random forest method achieved the best performance between the applied methods and succeeded in identifying the interactions between the features in the personal loan data.