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Uncovering Underlying Determinants of Bank Revenue

Tran, Julia LU and Tieu, Cindy LU (2024) DABN01 20241
Department of Statistics
Department of Economics
Abstract (Swedish)
For banks to remain resilient in times of economic instability, and what the Chair of the Supervisory Board of the European Central Bank refers to as; “radical uncertainty”, it is essential for banks to plan for the future. Challenges such as rising interest rates, climate change and geopolitical conflicts require proactive measures. Thus, accurately predicting bank revenue plays a crucial role in aiding banks in determining which strategies to implement and at what scale. Hence, this paper examines what bank-specific determinants influence revenue forecasting and prediction across different customer segments in Sweden using machine learning algorithms such as XGBoost and Random forest. Additionally, it analyses whether there are distinct... (More)
For banks to remain resilient in times of economic instability, and what the Chair of the Supervisory Board of the European Central Bank refers to as; “radical uncertainty”, it is essential for banks to plan for the future. Challenges such as rising interest rates, climate change and geopolitical conflicts require proactive measures. Thus, accurately predicting bank revenue plays a crucial role in aiding banks in determining which strategies to implement and at what scale. Hence, this paper examines what bank-specific determinants influence revenue forecasting and prediction across different customer segments in Sweden using machine learning algorithms such as XGBoost and Random forest. Additionally, it analyses whether there are distinct features influencing revenue among the segments and how macroeconomic indicators contribute to enhancing the accuracy of revenue forecasts. This study utilizes a dataset spanning from 2019 to 2023 provided by a major Swedish bank. The main findings of this paper is that the key drivers of bank revenue forecasting across all customer segments include "transaction amount," "maximum limit amount," "age," "consumer loan application amount," and "probability of default." Furthermore, it was found that macroeconomic determinants do not significantly enhance the accuracy of revenue forecasting. (Less)
Please use this url to cite or link to this publication:
author
Tran, Julia LU and Tieu, Cindy LU
supervisor
organization
course
DABN01 20241
year
type
H1 - Master's Degree (One Year)
subject
keywords
machine learning, bank revenue, macroeconomic indicators, revenue forecasting, revenue prediction
language
English
id
9176860
date added to LUP
2025-02-13 08:54:37
date last changed
2025-02-13 08:54:37
@misc{9176860,
  abstract     = {{For banks to remain resilient in times of economic instability, and what the Chair of the Supervisory Board of the European Central Bank refers to as; “radical uncertainty”, it is essential for banks to plan for the future. Challenges such as rising interest rates, climate change and geopolitical conflicts require proactive measures. Thus, accurately predicting bank revenue plays a crucial role in aiding banks in determining which strategies to implement and at what scale. Hence, this paper examines what bank-specific determinants influence revenue forecasting and prediction across different customer segments in Sweden using machine learning algorithms such as XGBoost and Random forest. Additionally, it analyses whether there are distinct features influencing revenue among the segments and how macroeconomic indicators contribute to enhancing the accuracy of revenue forecasts. This study utilizes a dataset spanning from 2019 to 2023 provided by a major Swedish bank. The main findings of this paper is that the key drivers of bank revenue forecasting across all customer segments include "transaction amount," "maximum limit amount," "age," "consumer loan application amount," and "probability of default." Furthermore, it was found that macroeconomic determinants do not significantly enhance the accuracy of revenue forecasting.}},
  author       = {{Tran, Julia and Tieu, Cindy}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Uncovering Underlying Determinants of Bank Revenue}},
  year         = {{2024}},
}