Calculating Operational Risk Capital Craig Ivey Royal Bank of - PowerPoint PPT Presentation

Calculating Operational Risk Capital Craig Ivey Royal Bank of Scotland, Head of Operational Risk Appetite and Capital Disclaimer: this presentation represents the speaker's opinions and does not necessarily represent the views of the RBS Group or its subsidiaries. Document Classification: Public

OpRisk Capital: Executive Summary Banks use a range of advanced tools, such as Economic Capital models, to enhance their management of risk and capital: • This presentation provides an introduction into Operational Risk Capital modelling and changes on course in light of regulatory developments. • The following questions will be addressed: 1. How do we currently estimate our Operational Risk Capital requirements? 2. Why are the simpler approaches for Operational Risk Capital under review? 3. What drives the risk based Operational Risk Capital estimates? Document Classification: Public 2

OpRisk Capital: Background Context Operational Risk is evolving, quantification has proven difficult: • Broad definition of Operational Risk (BCBS 2006); universally fundamental to a bank’s risk management framework. • Thought leadership to date highlights the on-going evolution of Operational Risk Management and need for further research and critical evaluation. • While some seek more advanced approaches, on going scrutiny of the internal model approach known as AMA has also become a common theme: – Particularly due to challenges standardising this approach (Mr Coen, 2015). 3 Document Classification: Public

OpRisk Capital: An Introduction The management of Operational Risk is one of the components of the Bank’s risk management framework: • We expect some level of Operational Risk losses as part of doing business, such “expected” losses are generally absorbed by income. • More severe losses (“unexpected” losses) are, however, to be absorbed by capital reserves. • Regulators set minimum levels of capital reserves to support financial stability. • Additional Economic Capital is held where appropriate. • Total capital requirements, including Economic Capital, are assessed through the bank’s capital risk assessment known (in the UK) as the Internal Capital Adequacy Assessment Process (ICAAP). Document Classification: Public 4

OpRisk Capital: Loss Themes Significant Operational Risk losses have arisen due to the following: • Mis-selling of complex products: – Mortgage Backed Securities fines and settlements e.g. JP Morgan’s $13bn. – Payment Protection Insurance mis-selling losses of £21.8bn have been incurred since 2011 due to the length of time which mis-selling occurred. – Use of deceptive marketing practices, involving breach of law and/or regulations; e.g. Bank of America’s $8.4bn settlement. Ineffective controls within operating processes allowed for: • Rogue trader losses on the back of market movements; e.g. Societe Generale € 4.9bn loss. • Losses arising from the unsuitably leveraging and / or off-balance sheet exposures; e.g. Bernard Madoff Ponzi scheme, which cost hedge fund investors $50bn. Document Classification: Public 5

OpRisk Capital: How do we Calculate? Banks are increasingly expected to self-assess their Operational Risk capital requirements during normal and ‘stress’ periods: • There are two simpler approaches to calculate minimum Operational Risk capital (BIA and the TSA). • BIA and TSA are based on three years Gross Income multiplied by a set of percentage rates (15% for BIA and 12% – 18% for TSA). • A third approach uses a risk-based statistical method; it is known as the AMA. • Operational Risk Economic Capital models are often built to AMA standard. – Risk based approaches use inputs directly from the bank’s Operational Risk framework and external loss data. – In order to capture risks not fully identified or measured by these simpler approaches, bank’s can use Economic Capital models if not AMA. • Bank’s separately consider risk during ‘stress conditions’ as part of formal regulatory Stress Test and our financial planning process. • Comparative analysis is performed as part of benchmarking process. Document Classification: Public 6

OpRisk Capital: Simpler Approaches Under Review The following criticisms of BIA / TSA have emerged: • The use of Gross Income incorrectly assumes a direct relationship between income and Operational Risk losses. • Gross Income is historic and so fails to provide a forward looking view. The Basel Committee intends to consult on a new standardised approach termed the Standardised Measurement Approach: • This will be a new paper developing upon the current consultation paper which was set out in October 2014. • Capital may be flexed up or down based upon organisational size. • Many banks already cover BIA / TSA limitations using the Economic Capital model and / or other capital buffers. Document Classification: Public 7

OpRisk Capital: Economic Capital Model Brief overview of a ‘standard’ Operational Risk Economic Capital Model: • An Operational Risk quantification system is normally built to AMA standards (e.g to a 99.9th percentile soundness). • This includes four key data elements; internal & external loss event data, scenario analysis and risk and control factors (BEICF). • A range of capital measures (confidence intervals) and allocations (of differing granularity and risk focus) can be devised in practice: – A range of confidence intervals can provide a range of risk thresholds / limits. • Assumptions are made regarding model granularity, diversification benefits and correlation structures: – Work best if meaningful and inform risk management; insight into risk relationships provides insight into risk management. • Such a design would facilitate a range of Risk Appetite measures such as risk limits and capital allocation e.g. settling gross income to loss limits. 8 Document Classification: Public

OpRisk Capital: Loss Distribution Approach Economic Capital models often use industry standard approach for modelling Operational Risk: • The model provides a risk estimate for each risk unit. • The severity and frequency of events is estimated for the next year based upon Internal Loss Data (ILD) and External Loss Data (ELD). • We can then read off the confidence level as desired, standards are very high so we aim to be 99.9% confident that we will have sufficient capital. Document Classification: Public 9

OpRisk Capital: Model Components The following key inputs are required to run the model: • ILD provides the starting risk profile reflecting the bank’s actual loss experience. • ELD from the ORX database addresses the question ‘could it happen here’? • Internally generated Scenario Analysis provides a forward looking risk view which improves the model’s assumptions where appropriate e.g. by adjusting the severity estimates: • Risk & control factors (BEICFs) are adjustments that reflects the state of the control environment within the bank. Document Classification: Public 10

OpRisk Capital: Forward Looking View The forward looking view of risk: • This is primarily done through scenario analysis and BEICFs. • BEICFs can capture more granular risk and performance incentives. • Scenario analysis is the use of expert opinion in conjunction with external data to evaluate exposure to high-severity events (BCBS 2006). • Scenarios can identify emerging tail end risk by allowing ‘what ifs’ analysis which provides both risk management and measurement benefits. 11 Document Classification: Public

OpRisk Capital: Model Granularity and Allocations ORX 2015: Capital is usually modelled at a combination of business line / event type level (83% of respondents) and then allocated to individual business units: • Capital allocation varies across participants using different factors smoothed over 3-5 years for stability. • Allocation can be model based or use more subjective approaches. • Allocation metrics include; management accounting parameters, loss frequency / severity, restricted to large events, income or revenue, FTE and assets. • It’s desirable that an understandable risk sensitive allocation approach is used. 12 Document Classification: Public

OpRisk Capital: Model Reliance? Qualitative limits, top of house endorsement and the right risk taking culture are key enablers for any quantitative solution: • The Operational Risk framework must be constructed with complementary qualitative and quantitative elements. • Model validation (Model Risk Management), verification (Audit) and governance (Senior Management) are key elements to reduce model risk. • Must also consider ‘use test’ and how this is demonstrated. • Other benchmarks e.g. challenger models, scenario analysis, back-testing etc. remain important. 13 Document Classification: Public