Direct marketing campaigns in retail banking with the use of deep learning and random forests

Credit products are a crucial part of business of banks and other financial institutions. A novel approach based on time series of customer's data representation for predicting willingness to take a personal loan is shown. Proposed testing procedure based on moving window allows detection of complex, sequential, time based dependencies between particular transactions. Moreover, this approach reduces noise by eliminating irrelevant dependencies that would occur due to the lack of time dimension analysis. The system for identifying customers interested in credit products, based on classification with random forests and deep neural networks is proposed. The promising results of empirical studies prove that the system is able to extract significant patterns from customers historical transfer and transactional data and predict credit purchase likelihood. Our approach, including the testing method, is not limited to banking sector and can be easily transferred and implemented as a general purpose direct marketing campaign system. (C) 2019 Elsevier Ltd. All rights reserved.