Forecasting Cash Withdrawals at Service Counters in Savings Cooperative Using Morning Transaction Data and Machine Learning
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Abstract
Efficient cash reserve management is a critical factor in the operations of savings cooperatives, particularly for service counters that must ensure sufficient cash availability for members' transactions. This study proposes the development of machine learning models to forecast daily cash withdrawal for service counters. The approach utilizes machine learning techniques to classify withdrawal amounts into two categories: Not exceeding 3 million THB and more than 3 million THB, aiming to enhance cash management efficiency and reduce cash holding costs. The dataset comprises two years of historical counter withdrawal transactions from a savings cooperative in Thailand. The model employs morning withdrawal amounts and previous-day withdrawals as independent variables for prediction. This study evaluates the performance of Gradient Boosting, Logistic Regression, and Multi-Layer Perceptron (MLP) models and examines the impact of different time intervals on prediction accuracy. The results indicate that the MLP model achieved the highest accuracy of 84.80% when utilizing withdrawal data from 08:00 to 12:00, demonstrating its effectiveness in optimizing cash reserve forecasting.
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References
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