Agent Based Simulation of Debt Resistance in Digital Lending Markets in Africa

The rise of digital lending platforms in Sub-Saharan Africa has significantly improved access to short-term credit, especially for the financially excluded. However, this growth has been accompanied by rising borrower defaults, debt distress and adaptive debt-resistance behaviors such as strategic defaults, loan stacking and platform hopping. Existing scholarship largely reports these dy-namics ex post, using econometric or qualitative techniques that provide little foresight into how alternative regulatory interventions could change market outcomes. In this paper, we introduce an agent-based modeling (ABM) frame-work that serves as a policy laboratory for digital lending regulation in African contexts. The key regulatory levers, including interest rate caps, credit expo-sure limits, cooling-off periods and transparency requirements are systematically assessed. Simulation results show that punishment approaches alone in-crease adaptive resistance, with increased systemic default risk and lender exit. In contrast, layering regulatory strategies with behavioral interventions and moderate credit constraints reduces prevalence of resistance while maintaining financial inclusion.