  Matching companies and investors is usually considered a highly specialized
decision making process. Building an AI agent that can automate such
recommendation process can significantly help reduce costs, and eliminate human
biases and errors. However, limited sample size of financial data-sets and the
need for not only good recommendations, but also explaining why a particular
recommendation is being made, makes this a challenging problem. In this work we
propose a representation learning based recommendation engine that works
extremely well with small datasets and demonstrate how it can be coupled with a
parameterized explanation generation engine to build an explainable
recommendation system for investor-company matching. We compare the performance
of our system with human generated recommendations and demonstrate the ability
of our algorithm to perform extremely well on this task. We also highlight how
explainability helps with real-life adoption of our system.
