  Over the last couple of decades in the lending industry, financial
disintermediation has occurred on a global scale. Traditionally, even for small
supply of funds, banks would act as the conduit between the funds and the
borrowers. It has now been possible to overcome some of the obstacles
associated with such supply of funds with the advent of online platforms like
Kiva, Prosper, LendingClub. Kiva for example, works with Micro Finance
Institutions (MFIs) in developing countries to build Internet profiles of
borrowers with a brief biography, loan requested, loan term, and purpose. Kiva,
in particular, allows lenders to fund projects in different sectors through
group or individual funding. Traditional research studies have investigated
various factors behind lender preferences purely from the perspective of loan
attributes and only until recently have some cross-country cultural preferences
been investigated. In this paper, we investigate lender perceptions of economic
factors of the borrower countries in relation to their preferences towards
loans associated with different sectors. We find that the influence from
economic factors and loan attributes can have substantially different roles to
play for different sectors in achieving faster funding. We formally investigate
and quantify the hidden biases prevalent in different loan sectors using recent
tools from causal inference and regression models that rely on Bayesian
variable selection methods. We then extend these models to incorporate fairness
constraints based on our empirical analysis and find that such models can still
achieve near comparable results with respect to baseline regression models.
