Post GFC review it was recognised that the significant complexity of modelling

Post gfc review it was recognised that the

This preview shows page 40 - 42 out of 42 pages.

Post GFC review it was recognised that the significant complexity of modelling operational risks had led to variation in capital requirements across banks and excessive use of resources on measuring capital rather than managing the risk. Under Basel III to be implemented in 2022, banks will use a standardised approach where the amount or capital will be based on easily verifiable accounting data representing their business sources of income, scaled by a factor based on 10 years of high quality ILD . It is expected that banks will use their modelling resources more to pre-emptively identify and control operational risks, eg. application of artificial intelligence on big data to improve identification of internal fraud and suspicious transactions. Scaling capital required by internal loss data will penalise banks that have large losses with additional regulatory capital.
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41 Factors that influence the effective management of operational risks Successful management of operational risks depends on: a strong risk culture and active engagement by the Board the comprehensiveness of recognition of operational risks in the risk governance framework: first line must include all parts of the organisation that cause risk (eg. IT) and not just the profit centres and the second line must include all specialists that have risk knowledge eg. HR and legal departments) clear accountabilities and ownership of operational risks supported by a comprehensive and consistent risk taxonomy operational risk is included in the risk criteria and has an agreed level of tolerance quality of monitoring reporting and quality of predictive measures of risks based on root cause analysis (KRIs) and not only reporting of historical incidents after the event quality of internal loss data (incidents and near misses) and adoption of quantitative analytics applied to risk identification and forward-looking risk assessment (eg. AI applied to fraud detection) quality of control effectiveness assurance testing and speed of remediation of defective controls Reading: McKinsey, Nonfinancial risk: A growing challenge for the bank, 2016.
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