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.

Di Vincenzo, D., Greselin, F., Piacenza, F., Zitikis, R. (2024). A Tweet Data Analysis for Detecting Emerging Operational Risks. In M. Corazza (a cura di), Mathematical and Statistical Methods for Actuarial Sciences and Finance MAF2024 Conference proceedings (pp. 136-142). SPRINGER INTERNATIONAL PUBLISHING AG [10.1007/978-3-031-64273-9_23].

A Tweet Data Analysis for Detecting Emerging Operational Risks

Greselin, F;Piacenza, F
;
2024

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.
Capitolo o saggio
clustering; early warning; emerging OpRisks; natural language processing; operational risk; text analysis; tweets
English
Mathematical and Statistical Methods for Actuarial Sciences and Finance MAF2024 Conference proceedings
Corazza, M., Gannon, F., Legros, F., Pizzi, C., Touzè, V
2-ago-2024
2024
9783031642722
MAF 2024
SPRINGER INTERNATIONAL PUBLISHING AG
136
142
Di Vincenzo, D., Greselin, F., Piacenza, F., Zitikis, R. (2024). A Tweet Data Analysis for Detecting Emerging Operational Risks. In M. Corazza (a cura di), Mathematical and Statistical Methods for Actuarial Sciences and Finance MAF2024 Conference proceedings (pp. 136-142). SPRINGER INTERNATIONAL PUBLISHING AG [10.1007/978-3-031-64273-9_23].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/525705
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