Factors influencing the use of modern technology by microfinance banks and Credit-only microfinance institutions

Abstract

Financial inclusion aims to ensure that everyone, including the poor, has access to financial services, thereby promoting economic growth and development. Financial institutions have adopted new technologies to accelerate financial inclusion. These technologies include cloud computing, blockchain, artificial intelligence, machine learning, deep learning and robotic process automation. However, given that technology adoption depends on various aspects from the rate of technological changes, institutional features, products and even the nature of clients for microfinance banks and institutions, there is little empirical evidence on the rate of adoption and relevance of new technologies by microfinance banks and credit only institutions. The three main objectives of the study were first, to assess the level of adoption of new technologies in financial inclusion, identify organizational features that influence the type of new technologies adopted and obtain the perspectives of the microfinance banks and credit-only institutions on these new technologies. This study was anchored on Diffusion of Innovation and Financial Intermediation theories, The main population was 13 Microfinance banks licensed by the Central Bank of Kenya as at December 2022 and the 34 Credit only microfinance institutions as listed by the Association of Microfinance Institutions in Kenya in 2022. Primary data was obtained using an online questionnaire and secondary data was obtained from available annual reports for 2022. Both descriptive and multivariate analysis were carried out aided by multinomial logistic regression to establish the organizational factors that may influence the adoption of new technologies. Response was obtained from 39 organizations. Key findings were that all organizations have adopted artificial intelligence, which is ranked as the best technology to promote financial inclusion. However, even though other technologies have been adopted, robotic process automation was the least adopted. Board size reported a significant and positive association with machine learning technology, while profitability, poor asset quality and capital adequacy reported a significant and positive association with deep learning technology. Microfinance banks have adopted cloud computing at a lower rate as compared with credit only microfinance institutions, while MFBs adopt deep learning at a higher rate than that of Credit Only Microfinance institution. Finally, more older organizations have adopted cloud computing as compared to the younger ones. Respondents explained that the main motivation for adopting new technologies was to expand the customer base and reduce operational costs. However, the major challenge of adopting new technologies was costs, due to resource constraints by Microfinance banks and Credit only financial institutions. The key concern for respondents was the fact that customers prioritize using new technology to borrow, with little use of the other services in financial inclusion. These findings are important as they provide empirical evidence on the best technology that aids financial inclusion and areas where key stakeholders can focus to enhance the use of new technologies to promote financial inclusion. Further studies are necessary to include all stakeholders in financial inclusion, with main stakeholder being the customer, to determine the customer experience.