The Impact of Cashless Policy on the Performance of Msmes in Nigeria Using Artificial Neural Network

Abstract

This study investigates the impact of cashless policy on the performance of Micro, Small, and Medium-Sized Enterprises (MSMEs) in Suleja, Nigeria, using Neural Network regression model. A survey research design was employed to collect data from 400 MSMEs, which were segmented into three clusters based on their cashless payment system adoption using Artificial Neural Network Clustering algorithm. The results show that the MSMEs were segmented into three clusters, with Cluster 1 having high adoption (n=150, 37.5%), Cluster 2 having moderate adoption (n=120, 30%), and Cluster 3 having low adoption (n=130, 32.5%). The study found that the adoption of cashless payment systems has a significant positive impact on the financial performance of MSMEs, with Cluster 1 having the highest financial performance (mean profit margin = 25.6%, SD = 5.2). Neural network regression model was used to predict business performance metrics, with a moderate level of predictive performance (MSE = 2.867, RMSE = 1.693, MAE = 1.693, MAPE = 42.33%). Feature importance analysis reveals that Health/Pharmacies and Repair of Home Gadgets are the most important features, with a mean dropout loss of 1.353. The findings of this study are important for policymakers, business owners, and researchers in the areas of financial inclusion, digital payments, and MSME development. The study highlights the potential benefits of cashless policy on the financial performance of MSMEs and identifies key factors that influence the adoption of cashless payment systems. The results can inform policy interventions and business strategies aimed at promoting financial inclusion and improving the performance of MSMEs in Nigeria and similar contexts.