| Abstract: |
Microfinance in developing areas such as Africa has been proven to improve the
local economy significantly. However, many applicants in developing areas
cannot provide adequate information required by the financial institution to
make a lending decision. As a result, it is challenging for microfinance
institutions to assign credit properly based on conventional policies. In this
paper, we formulate the decision-making of microfinance into a rigorous
optimization-based framework involving learning and control. We propose an
algorithm to explore and learn the optimal policy to approve or reject
applicants. We provide the conditions under which the algorithms are
guaranteed to converge to an optimal one. The proposed algorithm can naturally
deal with missing information and systematically tradeoff multiple objectives
such as profit maximization, financial inclusion, social benefits, and
economic development. Through extensive simulation of both real and synthetic
microfinance datasets, we showed our proposed algorithm is superior to
existing benchmarks. To the best of our knowledge, this paper is the first to
make a connection between microfinance and control and use control-theoretic
tools to optimize the policy with a provable guarantee. |