Identifying the Barriers to Digital Financial Inclusion in The Most Financially Excluded Country Using Machine Learning Algorithm

Despite the call of digital financial services (DFS) to improve inclusive growth and reduce poverty, the adoption of DFS remains low in Nigeria. The objective of this study is to examine the barriers of ability, access and usage of DFS in Nigeria. This study uses secondary data Global Findex year 2017 and year 2021 to predict the socioeconomic factors on the target variables of DFS (ability, access and usage). Using a machine learning (ML) algorithm, namely the J48 decision tree in the Waikato Environment for Knowledge Analysis (WEKA) software, this study analyses the predictive strength of variables such as gender, education, income quintile, employment status, and urbanicity in determining ability, access to and usage of DFS. The main findings from the results show that the J48 decision tree demonstrates improvement in correctly classifying instances for year 2017 data to the year 2021 data. The root nodes for all sets of data show that education is the main predictor for DFS. The first-level split is gender for DFS when the target variables are ability and usage but is age when the target variable is access. Results show that education is the main barrier of DFS whereas gender and age are the secondary impediments to the adoption of DFS. Policymakers can benefit from the findings of this study to design targeted interventions—such as increasing their education level and organizing more digital financial literacy programs to accelerate DFS adoption among marginalized groups. The novelty of this study is to utilize a ML algorithm to identify the barriers of DFS and its accuracy rate has increased from the results of using the year 2017 data to the year 2021 data. By exploring key determinants through ML, this study contributes to the broader agenda of financial inclusion and promotes the accomplishment of sustainable development goals.