ATM cash demand forecast

Keywords: cash, demand

Background

Automated Teller Machines (ATMs) are the kind of computer for specialized tasks that reduces bank visits and lets the customer complete the financial transaction. ATM plays a crucial role in providing convenient and accessible cash services to bank customers. To ensure efficient cash management and optimize operational costs, accurate forecasting of ATM cash demand is essential. This paper proposes an ATM cash demand forecasting model using Artificial Neural Networks (ANN). The proposed model utilizes historical ATM transaction data, including cash withdrawal patterns, temporal factors, and contextual variables, to train the ANN. The ANN architecture consists of multiple interconnected layers of artificial neurons, capable of learning complex patterns and relationships within the data. To enhance the forecasting accuracy, the model incorporates additional features such as the day of the week, holidays, and special events that may influence cash demand. The training process involves the iterative adjustment of network weights to minimize the prediction error. The model is then tested on a separate dataset to evaluate its performance.

Aim & Objectives

Aim
To forecast ATM cash demand Using DL Technique
Objective
Develop an accurate ATM cash demand forecasting model using Artificial Neural Networks (ANN) to leverage historical transaction data, temporal factors, and contextual variables, aiming to optimize cash management processes in banks. The model's objective is to provide precise predictions that guide effective cash replenishment strategies, cost reduction, and improved cash availability, ultimately enhancing customer satisfaction and operational efficiency in ATM services.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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