Development of Machine Learning based Cash Forecasting Models for Automated Teller Machines

Uygar Er; Ceren Ulus; Nazlı Yusufoğlu; Mehmet Fatih Akay

Makale detayları

Development of Machine Learning based Cash Forecasting Models for Automated Teller Machines

Yazarlar:

Uygar Er | Ceren Ulus | Nazlı Yusufoğlu | Mehmet Fatih Akay

Yayıncı:

Cukurova University

Ana Başlıklar:

Software Engineering (Other)

Dergi:

Cukurova University Journal of Natural and Applied Sciences Vol.4 Issue 1, pp.35-44

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001352111

pISSN:

2822-2938

Anahtar kelimeler:

Özet:

 Nowadays, Automatic Teller Machines ATMs stand out as bank instruments where a high percentage of cash transactions take place. Accurately determining the amount of cash to be kept in ATMs is considered a strategic necessity for banks in terms of preventing service disruptions and maximizing customer satisfaction. Cash forecasting ensures that the amount of cash to be kept in ATMs is determined accurately. The aim of this study is to optimize cash management by forecasting daily cash demand in ATMs and thus help financial institutions prevent inefficiencies caused by cash depletion in ATMs and reduce customer dissatisfaction and operational costs. To achieve this, cash forecasting models have been developed using Extreme Gradient Boosting XGBoost. The performance of the models has been evaluated with the Percentage Error PE metric. The developed models provided error values lower than 15. A comprehensive evaluation has shown that accurate cash forecasts significantly increase the effectiveness of cash management.

 Nowadays, Automatic Teller Machines ATMs stand out as bank instruments where a high percentage of cash transactions take place. Accurately determining the amount of cash to be kept in ATMs is considered a strategic necessity for banks in terms of preventing service disruptions and maximizing customer satisfaction. Cash forecasting ensures that the amount of cash to be kept in ATMs is determined accurately. The aim of this study is to optimize cash management by forecasting daily cash demand in ATMs and thus help financial institutions prevent inefficiencies caused by cash depletion in ATMs and reduce customer dissatisfaction and operational costs. To achieve this, cash forecasting models have been developed using Extreme Gradient Boosting XGBoost. The performance of the models has been evaluated with the Percentage Error PE metric. The developed models provided error values lower than 15. A comprehensive evaluation has shown that accurate cash forecasts significantly increase the effectiveness of cash management.

 Nowadays, Automatic Teller Machines ATMs stand out as bank instruments where a high percentage of cash transactions take place. Accurately determining the amount of cash to be kept in ATMs is considered a strategic necessity for banks in terms of preventing service disruptions and maximizing customer satisfaction. Cash forecasting ensures that the amount of cash to be kept in ATMs is determined accurately. The aim of this study is to optimize cash management by forecasting daily cash demand in ATMs and thus help financial institutions prevent inefficiencies caused by cash depletion in ATMs and reduce customer dissatisfaction and operational costs. To achieve this, cash forecasting models have been developed using Extreme Gradient Boosting XGBoost. The performance of the models has been evaluated with the Percentage Error PE metric. The developed models provided error values lower than 15. A comprehensive evaluation has shown that accurate cash forecasts significantly increase the effectiveness of cash management.

 Nowadays, Automatic Teller Machines ATMs stand out as bank instruments where a high percentage of cash transactions take place. Accurately determining the amount of cash to be kept in ATMs is considered a strategic necessity for banks in terms of preventing service disruptions and maximizing customer satisfaction. Cash forecasting ensures that the amount of cash to be kept in ATMs is determined accurately. The aim of this study is to optimize cash management by forecasting daily cash demand in ATMs and thus help financial institutions prevent inefficiencies caused by cash depletion in ATMs and reduce customer dissatisfaction and operational costs. To achieve this, cash forecasting models have been developed using Extreme Gradient Boosting XGBoost. The performance of the models has been evaluated with the Percentage Error PE metric. The developed models provided error values lower than 15. A comprehensive evaluation has shown that accurate cash forecasts significantly increase the effectiveness of cash management.

Makale detayları

Development of Machine Learning based Cash Forecasting Models for Automated Teller Machines

Yazarlar:

Uygar Er | Ceren Ulus | Nazlı Yusufoğlu | Mehmet Fatih Akay

Yayıncı:

Cukurova University

Ana Başlıklar:

Software Engineering (Other)

Dergi:

Cukurova University Journal of Natural and Applied Sciences Vol.4 Issue 1, pp.35-44

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001352111

pISSN:

2822-2938

Cash Forecasting

Cash Management

ATM

XGBoost

Sayı

Makale detayları

Development of Machine Learning based Cash Forecasting Models for Automated Teller Machines

Yazarlar: Uygar Er | Ceren Ulus | Nazlı Yusufoğlu | Mehmet Fatih Akay

Yayıncı: Cukurova University

Ana Başlıklar: Software Engineering (Other)

Dergi: Cukurova University Journal of Natural and Applied Sciences Vol.4 Issue 1, pp.35-44

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001352111

pISSN: 2822-2938

Cash Forecasting

Cash Management

ATM

XGBoost

Sayı

Yazarlar:

Uygar Er | Ceren Ulus | Nazlı Yusufoğlu | Mehmet Fatih Akay

Yayıncı:

Cukurova University

Ana Başlıklar:

Software Engineering (Other)

Dergi:

Cukurova University Journal of Natural and Applied Sciences Vol.4 Issue 1, pp.35-44

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001352111

pISSN:

2822-2938