A Tweet Data Analysis for Detecting Emerging Operational Risks

Davide Di Vincenzo (), Francesca Greselin (), Fabio Piacenza () and Ričardas Zitikis ()
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Davide Di Vincenzo: UniCredit S.p.A, Group Non Financial Risks
Francesca Greselin: Univ. Milano Bicocca, Department of Statistics and Quantitative Methods
Fabio Piacenza: UniCredit S.p.A, Group Non Financial Risks
Ričardas Zitikis: Western University, School of Mathematical and Statistical Sciences

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2024, pp 136-142 from Springer

Abstract: Abstract Operational risk (OpRisk) is emerging as a crucial non financial consideration with widespread implications for financial institutions. Shifting away from traditional regulatory tasks, including data collection, capital requirement calculations, and report generation for managerial decisions, OpRisk functions are now adopting proactive strategies to prevent or mitigate risks. The integration of Artificial Intelligence techniques, increasingly essential for managerial insights, is utilized to glean additional information from data. This study propels the utilization of text analysis techniques in the context of OpRisk. A pioneering dimension involves examining pertinent tweet content from social media X for the continuous monitoring of the evolving risk landscape, aiming to identify early warnings about new types of potentially risky events.

Keywords: clustering; early warning; emerging OpRisks; natural language processing; operational risk; text analysis; tweets (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-64273-9_23

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