Parameterized Explanations for Investor / Company Matching
This work addresses the domain-specific problem of improving efficiency and reducing bias in investor-company matching for financial applications, with incremental contributions in explainability.
The authors tackled the problem of automating investor-company matching with explainable recommendations, addressing challenges like small financial datasets and the need for justifications. They proposed a representation learning-based recommendation engine that performs well with limited data and couples it with a parameterized explanation generator, demonstrating strong performance compared to human recommendations.
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.