Machine learning for categorization of operational risk events using textual description

Suren Pakhchanyan, Christian Fieberg, Daniel Metko and Thomas Kaspereit

Journal of Operational Risk

Abstract: This paper provides an overview of how machine learning can help in categorizing textual descriptions of operational loss events into Basel II event types. We apply PYTHON implementations of support vector machine and multinomial naive Bayes algorithms to precategorized Öffentliche Schadenfälle OpRisk (ÖffSchOR) data to demonstrate that operational loss events can be automatically assigned to one of the seven Basel II event types with very few costs and satisfactory accuracy. Our comprehensive case study on ÖffSchOR data, which includes the provision of parsimonious PYTHON code, is also useful for practitioners, who can use this knowledge to improve the cost efficiency and/or reliability of their processes for categorizing operational risk events.

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Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ3:7955548

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