Fintech adoption dynamics in a pandemic : an experience from some financial institutions in Nigeria during COVID-19 using machine learning approach

The novel coronavirus caused a lifestyle shift, and the acceptance of offsite financial transactions is still a case for financial technology (fintech). Mobile financial transactions continue to be at an all-time low, and financial institutions are developing approaches for financial digitalization acceptability. The present study attempts to understand users' motivations for fintech adoption. The technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTUAT) were utilized to uncover the rationale behind technology adoption. This study explored the drivers inhibiting the adoption of financial technology in Nigeria during the pandemic. A machine learning (ML) approach was implemented to examine fintech adoption predictors using a self-administered consumer survey of 480 account holders. Survey responses were analyzed using a set of ML models (naïve Bayes, logistic regression, K-nearest neighbors, decision trees, and support vector machines), revealing the features and decision criteria for predicting perceived technology adoption. The decision tree outperformed the other models, with an accuracy of over 84%, precision of 88%, recall of 86%, F1-score of 84%, and area under the curve of 87%. The result indicates that customers are concerned about their safety. Thus, furthering their sense of risk. These results provide a roadmap for financial institutions and policymakers to understand behavioral attitudes toward adopting fintech and suggest strategies for attracting customers to the fintech space.