Abstract
Forecasting deposits as balance in a customer’s payment account is one of the most important bank problems. Accurate forecasting helps manage risks, plan investment policies, adjust interest rates, marketing and customer care. With the increasingly powerful application of information systems to collect full customer data and the miraculous ability of artificial intelligence, the problem of forecasting payment account balances can be solved. This paper used AutoRegressive Integrated Moving Average (ARIMA), Long short term memory (LSTM) and Hierarchical Forecasting (HF) methods to forecast customer account balance, while focusing on analysing effects from customer clustering on the predicted results. Research has shown that the forecast of the series of total balances can be based on the results of predictions on the group. At the same time, we can quickly detect anomalies in groups and devise appropriate remedies.
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Phan, D.H., Do, Q.D. (2020). Analysing Effects of Customer Clustering for Customer’s Account Balance Forecasting. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_20
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