AI-Driven Microfinance: Transforming Financial Inclusion, Organizational Performance, and Economic Development

Abstract

Artificial intelligence opens up the potential for linkage between inclusive financial services and workforce capability and institutional performance. This study employs an integrative literature review, as well as original quantitative analysis of publicly available data from MIX Market and Global Findex. In FY 2018, there are 753 microfinance providers in the institutional cross-section, and 212 institutions in the balanced workforce panel are followed from 2011 to 2018. A total of 171 providers report human resource policies which are analysed. A retrospective predictive benchmark is a classification of operational self-sufficiency in the next annual record based on information from financial, outreach, and workforce sources from the previous year. There are 11,529 training observations and 577 validation observations used for model development, and 528 institutions reporting in calendar year 2018 used for final testing. The area under the receiver operating characteristic curve of the random forest that includes information on the workforce is 0.858, whereas a logistic model using prior performance served as a baseline with an area under the curve of 0.818. A matched financial-and-outreach forest has a value of 0.862, and offers an explicit assessment of the workforce specification. The median operational self-sufficiency of the institutional results is 114.90%, while 78.95% and 74.27% of the social-reporting institutions document social-protection and grievance-resolution policies, respectively. In developing economies, global Findex aggregates rose by 27.65 percentage points in 2014-24 in digital-payment participation. Four figures and six tables link the empirical results to human resource management, labour organisation, operation, industrial relations and financial inclusion. The study provides a replicable approach to assess predictive decision support and align technological capability with people, processes and productive financial involvement.