Refugees in protracted displacement often exhibit entrepreneurial activity yet face systemic barriers to formal financial services. The proliferation of mobile money in East Africa presents a potential avenue for financial inclusion, but its efficacy within constrained refugee camp economies is poorly understood. This study aimed to investigate the specific barriers refugees face in accessing mobile credit and to co-design and implement a practical intervention with a mobile network operator to improve loan eligibility for residents of the Kakuma camp complex. An action research methodology was employed, involving iterative cycles of diagnostic research, planning, intervention, and evaluation. Data were collected via focus group discussions with refugee entrepreneurs, in-depth interviews with mobile money agents and the operator's risk analysts, and analysis of anonymised transaction data. A primary barrier identified was the near-universal (over 95%) automatic rejection of loan applications due to a lack of formal credit history and the system's inability to verify non-Kenyan identification. The co-designed intervention, which incorporated alternative data from mobile airtime purchases and refugee-specific merchant networks, led to a pilot approval rate of 34% for a test cohort. Refugee status creates a distinct 'paradox' where economic activity is visible locally but invisible to automated credit scoring systems. Action research demonstrates that contextual adaptations to fintech underwriting can significantly enhance access. Financial service providers should develop refugee-sensitive product frameworks using alternative data. Policymakers must support the creation of legal identities recognised by financial technology systems. Further research should explore the long-term sustainability and repayment performance of such adapted credit models. financial inclusion, refugees, mobile money, credit scoring, action research, Kenya, humanitarian finance This paper provides novel empirical evidence on the mechanics of digital credit exclusion in a refugee camp and presents a tested, alternative data framework for improving loan assessment in such contexts.
Ochieng et al. (2015) studied this question.