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Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report. / Cornwell, Nikki; Bilson, Christoper; Gepp, Adrian et al.
In: Pacific-Basin Finance Journal, Vol. 77, 101906, 02.2023.

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Cornwell N, Bilson C, Gepp A, Stern S, Vanstone B. Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report. Pacific-Basin Finance Journal. 2023 Feb;77:101906. Epub 2022 Dec 2. doi: 10.1016/j.pacfin.2022.101906

Author

Cornwell, Nikki ; Bilson, Christoper ; Gepp, Adrian et al. / Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report. In: Pacific-Basin Finance Journal. 2023 ; Vol. 77.

RIS

TY - JOUR

T1 - Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report

AU - Cornwell, Nikki

AU - Bilson, Christoper

AU - Gepp, Adrian

AU - Stern, Steven

AU - Vanstone, Bruce

PY - 2023/2

Y1 - 2023/2

N2 - To enable more proactive management of the underlying sources of operational risks in financial institutions, this pre-registered study seeks to improve traditional qualitative approaches to causal factors analysis. A Bayesian network-based approach is used to leverage both incident and operations data to model the probability of operational loss events. The approach is applied and empirically tested in a case study on an Australian insurance company. The outputs from the model go beyond simply identifying key risk drivers to offer risk managers a deeper understanding of how causal factors influence risk. Insights into the collective effects of causal factors, their relative importance and critical thresholds strategically inform more efficient and effective mitigation decisions, ultimately enhancing firm performance and value.

AB - To enable more proactive management of the underlying sources of operational risks in financial institutions, this pre-registered study seeks to improve traditional qualitative approaches to causal factors analysis. A Bayesian network-based approach is used to leverage both incident and operations data to model the probability of operational loss events. The approach is applied and empirically tested in a case study on an Australian insurance company. The outputs from the model go beyond simply identifying key risk drivers to offer risk managers a deeper understanding of how causal factors influence risk. Insights into the collective effects of causal factors, their relative importance and critical thresholds strategically inform more efficient and effective mitigation decisions, ultimately enhancing firm performance and value.

U2 - 10.1016/j.pacfin.2022.101906

DO - 10.1016/j.pacfin.2022.101906

M3 - Article

VL - 77

JO - Pacific-Basin Finance Journal

JF - Pacific-Basin Finance Journal

SN - 0927-538X

M1 - 101906

ER -