Individual revenue forecasting in the banking sector
This paper analyses data from a Swedish bank combined with macroeconomic indicators to forecast revenues for individual customers over the course of four years. Separate models are created for recurring customers and customers who have just joined the bank. XGBoost is shown to outperform linear regression, random forest, neural network and support vector regression when comparing both mean absolut
