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Individual revenue forecasting in the banking sector

Pinto Brandão, Ricardo Jorge LU and Sulzickyte, Simona LU (2023) DABN01 20231
Department of Economics
Department of Statistics
Abstract
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 absolute error and mean squared error. Macroeconomic variables reveal little to no significance for such forecasts. Finally, a cluster-based method is proposed where customers are first assigned a cluster and then different models are trained for each cluster. We conclude that such a method is only effective if the classification of customers into their... (More)
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 absolute error and mean squared error. Macroeconomic variables reveal little to no significance for such forecasts. Finally, a cluster-based method is proposed where customers are first assigned a cluster and then different models are trained for each cluster. We conclude that such a method is only effective if the classification of customers into their respective cluster is sufficiently accurate. (Less)
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author
Pinto Brandão, Ricardo Jorge LU and Sulzickyte, Simona LU
supervisor
organization
course
DABN01 20231
year
type
H1 - Master's Degree (One Year)
subject
keywords
Revenue forecasting, Banking, Machine Learning, XGBoost, Customer segmentation
language
English
id
9123232
date added to LUP
2023-11-21 12:54:30
date last changed
2023-11-21 12:54:30
@misc{9123232,
  abstract     = {{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 absolute error and mean squared error. Macroeconomic variables reveal little to no significance for such forecasts. Finally, a cluster-based method is proposed where customers are first assigned a cluster and then different models are trained for each cluster. We conclude that such a method is only effective if the classification of customers into their respective cluster is sufficiently accurate.}},
  author       = {{Pinto Brandão, Ricardo Jorge and Sulzickyte, Simona}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Individual revenue forecasting in the banking sector}},
  year         = {{2023}},
}