Pricing and hedging aggregated micro-loan cashflows

Microfinance institutions (MFIs) play a critical role in providing access to capital to a large proportion of adult population in developing countries as well as in the developed world. According to a world bank report in 2017, a third of the world's adult population has no bank accounts and therefore rely on MFIs or lenders in informal economy for credit. Given the important role that MFIs continue to play in the wellbeing of society, it is imperative that they have appropriately tailored mathematical tools at their disposal for quantifying and mitigating financial risk posed by the business of lending funds to individuals with no credit history and no collateral.
The purpose of this project is to develop mathematical formulation for pricing and hedging financial derivative products for a pools of loans, taking into account of specific constraints that microloan lenders might have (large number of very small loans, no collateral etc). The project will build on two different strands of research in financial mathematics: the mathematics of pricing Collateralized Loan Obligations (CLOs) in developed markets and recent work on credit scoring for microloans in absence of customer credit history, using machine learning