  Assigning appropriate industry tag(s) to a company is a critical task in a
financial institution as it impacts various financial machineries. Yet, it
remains a complex task. Typically, such industry tags are to be assigned by
Subject Matter Experts (SME) after evaluating company business lines against
the industry definitions. It becomes even more challenging as companies
continue to add new businesses and newer industry definitions are formed. Given
the periodicity of the task it is reasonable to assume that an Artificial
Intelligent (AI) agent could be developed to carry it out in an efficient
manner. While this is an exciting prospect, the challenges appear from the need
of historical patterns of such tag assignments (or Labeling). Labeling is often
considered the most expensive task in Machine Learning (ML) due its dependency
on SMEs and manual efforts. Therefore, often, in enterprise set up, an ML
project encounters noisy and dependent labels. Such labels create technical
hindrances for ML Models to produce robust tag assignments. We propose an ML
pipeline which uses semantic similarity matching as an alternative to multi
label text classification, while making use of a Label Similarity Matrix and a
minimum labeling strategy. We demonstrate this pipeline achieves significant
improvements over the noise and exhibit robust predictive capabilities.
