Mobile Financial Services in Disaster Relief: Modeling Sustainability

  • David M. GarrityEmail author
Conference paper


Mobile Financial Services (MFS) have provided marginal populations with access to basic financial services, including savings programs and insurance policies. Disaster relief response has been characterized by the use of MFS as a vehicle for charitable donations, both directly from diaspora populations as well as campaigns organized by traditional relief organizations. The paper will develop a financial model and analysis of how scaling MFS can be commercially viable and sustainable. The analysis will assess the extent to which the deployment of MFS as a disaster risk mitigation measure may be enhanced by the provision of information on available risk profiles. The paper will assess the enabling environment for successful deployment of MFS as a mechanism for managing financial shocks in disaster relief and for mitigating individual risk. Statistical models have been developed using mobile network operator (MNO) call detail records (CDRs) to assess which subscribers may present better credit risks as well as how to best structure premium levels and payment methods to best fit subscribers’ abilities and needs. Based on such models and on the pricing structures of MFS, the paper will extrapolate from instances where farmers have secured insurance against weather-related crop failures and where MFS have been developed. MFS adoption in developing countries follows a model in which remittances lead to the adoption of other MFS. The overview indicates that the existence of established reciprocity and social networks drives volume and that establishment of trusted networks is critical to MFS achieving scale. In conclusion, the analysis will examine how patterns of use of MFS provide an informational basis on which disaster risk reduction can be implemented in different form factors.


Normalize Difference Vegetation Index Credit Risk Disaster Risk Disaster Relief Mobile Network Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author would like to thank Paula Lytle and Tim Kelly at the World Bank for feedback on an earlier presentation which provided impetus to this paper. Additional feedback from Charles Martin-Shields and Anne Nelson is gratefully appreciated. The author would also like to thank Maj Fiil-Flynn for her perspective on mobile money and Madeline Kleiner for her research support.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.GVA Research, LLCNew YorkUSA

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