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Dergiler / Cukurova University Journal of Natural and Applied Sciences / 2026 / Volume: 4 Issue: 1

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

Uygar Er(Innovance)0009-0003-5659-1241
Ceren Ulus(CUKUROVA UNIVERSITY)0000-0003-2086-6381
Nazlı Yusufoğlu(CUKUROVA UNIVERSITY)0009-0009-7331-7897
Mehmet Fatih Akay(CUKUROVA UNIVERSITY)0000-0003-0780-0679
Sayfa
—
Yayın Tarihi
2025-06-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

A comprehensive evaluation has shown that accurate cash forecasts significantly increase the effectiveness of cash management.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Nowadays, Automatic Teller Machines (ATMs) stand out as bank instruments where a high percentage of cash transactions take place.
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Temel Bulgular & Veri: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.
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Sonuç & Katkı:A comprehensive evaluation has shown that accurate cash forecasts significantly increase the effectiveness of cash management.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Cash Forecasting · Cash Management · ATM

Abstract

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.

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Bu Makaleye Atıf Yap

Er, U., Ulus, C., Yusufoğlu, N., Akay, M. F. (2025). Development of Machine Learning based Cash Forecasting Models for Automated Teller Machines. Cukurova University Journal of Natural and Applied Sciences, Volume: 4 Issue: 1. https://doi.org/10.70395/cunas.1680866

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Kaynakça (22)

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