  Financial institutions manage operational risk (OpRisk) by carrying out
activities required by regulation, such as collecting loss data, calculating
capital requirements, and reporting. For this purpose, for each OpRisk event,
loss amounts, dates, organizational units involved, event types, and
descriptions are recorded in the OpRisk databases. In recent years, operational
risk functions have been required to go beyond their regulatory tasks to
proactively manage operational risk, preventing or mitigating its impact. As
OpRisk databases also contain event descriptions, an area of opportunity is to
extract information from such texts. The present work introduces for the first
time a structured workflow for the application of text analysis techniques (one
of the main Natural Language Processing tasks) to the OpRisk event descriptions
to identify managerial clusters (more granular than regulatory categories)
representing the root-causes of the underlying risks. We have complemented and
enriched the established framework of statistical methods based on quantitative
data. Specifically, after delicate tasks like data cleaning, text
vectorization, and semantic adjustment, we have applied methods of
dimensionality reduction and several clustering models with algorithms to
compare their performances and weaknesses. Our results improve retrospective
knowledge of loss events and enable to mitigate future risks.
