Individual revenue forecasting in the banking sector
(2023) DABN01 20231Department 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)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9123232
- author
- Pinto Brandão, Ricardo Jorge LU and Sulzickyte, Simona LU
- supervisor
-
- Jonas Wallin LU
- organization
- course
- DABN01 20231
- year
- 2023
- 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}}, }