  Over the years, there has been a paradigm shift in how users access financial
services. With the advancement of digitalization more users have been
preferring the online mode of performing financial activities. This has led to
the generation of a huge volume of financial content. Most investors prefer to
go through these contents before making decisions. Every industry has terms
that are specific to the domain it operates in. Banking and Financial Services
are not an exception to this. In order to fully comprehend these contents, one
needs to have a thorough understanding of the financial terms. Getting a basic
idea about a term becomes easy when it is explained with the help of the broad
category to which it belongs. This broad category is referred to as hypernym.
For example, "bond" is a hypernym of the financial term "alternative
debenture". In this paper, we propose a system capable of extracting and
ranking hypernyms for a given financial term. The system has been trained with
financial text corpora obtained from various sources like DBpedia [4],
Investopedia, Financial Industry Business Ontology (FIBO), prospectus and so
on. Embeddings of these terms have been extracted using FinBERT [3], FinISH [1]
and fine-tuned using SentenceBERT [54]. A novel approach has been used to
augment the training set with negative samples. It uses the hierarchy present
in FIBO. Finally, we benchmark the system performance with that of the existing
ones. We establish that it performs better than the existing ones and is also
scalable.
