A text analysis for Operational Risk loss descriptions

Financial institutions manage operational risk by carrying out the activities required by regulation, such as collecting loss data, calculating capital requirements, and reporting. The information necessary for this purpose is then collected in the OpRisk databases. Recorded for each OpRisk event are loss amounts, dates, organizational units involved, event types and descriptions. In recent years, operational risk functions have been required to go beyond their regulatory tasks to proactively manage the operational risk, preventing or mitigating its impact. As OpRisk databases also contain event descriptions, usually defined as free text fields, an area of opportunity is the valorization of all the information contained in such records. As far as we are aware of, the present work is the first one that has addressed the application of text analysis techniques to the OpRisk event descriptions. In this way, we have complemented and enriched the established framework of statistical methods based on quantitative data. Specifically, we have applied text analysis methodologies to extract information from descriptions in the OpRisk database. After delicate tasks like data cleaning, text vectorization, and semantic adjustment, we apply methods of dimensionality reduction and several clustering models and algorithms to develop a comparison of their performances and weaknesses. Our results improve retrospective knowledge of loss events and enable to mitigate future risks.