Application of Neural Network for the Enhancement of Digital Marketing in Banking Sector

This study presents the design, implementation and evaluation of a Feedforward Neural Network (FFNN) aimed at enhancing digital marketing strategies in the banking sector by accurately predicting customer subscription behaviour. The artificial neuron model was defined using weighted inputs, bias parameters and a sigmoid activation function, forming the foundation for a multi-layer FFNN architecture optimized for learning efficiency. In order to address the issues of overfitting and improve generalization, a regularization technique which includes the traditional dropout algorithm were incorporated into the proposed model. The network was trained using the gradient descent back-propagation method for effective weight adjustment and big data analytics were employed to uncover customer financial patterns across quarterly periods, revealing the fourth quarter as the most favourable for product marketing due to higher account balances. A correlation matrix analysis showed weak linear relationships between input features and the target variable, validating the use of a non-linear model like FFNN. Experimental results demonstrated the model’s high predictive accuracy, with low error metrics (MAE: 0.003, MSE: 0.0013, RMSE: 0.000492), confirming the robustness and generalization ability of the trained network.