Authors:
Praveen Kumar Maroju

Addresses:
Department of Information Technology, Clientserver Technology Solutions, San Antonio, Texas, United States of America. praveen.maroju@clientservertech.com

Abstract:

Effective cash flow management in White Label ATMs (WLAs) ensures liquidity and reduces operational costs. This paper explores the application of data science and machine learning techniques to optimize the currency supply chain for WLAs. We developed predictive models using time series forecasting, regression models, and neural networks to forecast cash demand and optimize replenishment schedules by analyzing historical transaction data, seasonal patterns, and geographical influences. The proposed solution integrates these machine learning algorithms with traditional logistics to create a smarter and more responsive ATM network. Python, Pandas, Scikit-learn, and TensorFlow were used for data processing, model development, and evaluation. The dataset comprised three years of historical transaction data from a network of WLAs enriched with demographic and economic indicators. Our findings suggest that machine learning can significantly enhance the efficiency of cash distribution, minimize downtime, and reduce costs associated with cash handling and transportation.

Keywords: Data Science; Machine Learning; Cash Flow Optimization; White Label ATMs; Predictive Analytics; Cash Management; Market Conditions; Logistical Resources; Cash Demand.

Received on: 09/09/2023, Revised on: 02/12/2023, Accepted on: 05/02/2024, Published on: 01/03/2024

DOI: 10.69888/FTSCS.2024.000196

FMDB Transactions on Sustainable Computing Systems, 2024 Vol. 2 No. 1, Pages: 43-53

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