Operational risk modelling: common practices and future developments

Transcription

1 Operational risk modelling: common practices and future developments 22 July 2015 Caroline Coombe & Simon Cartlidge

2 Our workstream: IMV for Operational Risk ORIC International was approached by the IMIF to lead the work-stream related to operational risk. Rather than focus purely on validation (which would have overlapped with a number of the other work-streams), we decided to embrace a wider scope looking across the entire modelling process for this specialist area. This has involved gathering information on a range of areas relating to operational risk modelling: - Modelling approaches - Quantification techniques - Capital outputs - Documentation - Validation approaches

3 Our workstream: IMV for Operational Risk The primary sources of information for this booklet included: - Operational Risk Management and Measurement Survey with Oliver Wyman. - Discussions and outputs from ORIC International s capital modelling working and discussion groups. - ORIC International s Scenario Universe The views and knowledge of subject matter experts from ORIC International, its member firms, Oliver Wyman and the IMIF work-streams. - IMIF Steering Committee.

4 An introduction to ORIC International Founded in 2005, ORIC International is the leading operational risk consortium for the (re)insurance and asset management sector globally. The consortium currently consists of 40 members with accelerating international growth. ORIC International is a not-for-profit organisation dedicated to helping its members enhance the capabilities of their operational risk functions. We facilitate the anonymised and confidential exchange of operational risk intelligence between member firms; providing a diverse, high quality pool of quantitative and qualitative information on relevant operational risk exposures. As well as providing operational risk event data, ORIC International also provides industry benchmarks, undertakes leading edge research, sets trusted standards for operational risk and provides a forum for members to exchange ideas and best practice. Our comprehensive offering is designed to empower operational risk professionals to help the business and their Board in the identification, assessment, management/measurement, monitoring and reporting of operational risk.

5 Our members

6 Operational Risk Management & Measurement In December 2014, ORIC International partnered with Oliver Wyman to conduct a survey of ORIC s members, based on the Principles of Operational Risk Management & Measurement paper produced by the CRO Forum at the end of The aim was to assess current practices in operational risk management and measurement in the insurance industry. High-level topics included: - Governance and Risk Culture, - Framework, - RCSAs, - Scenario Analysis, - Modelling; and - Application of operational risk management and measurement. 30 members participated in the survey in total.

7 Operational Risk Management & Measurement Questions looking at the measurement of operational risk focussed on areas such as: - Granularity of quantification - Data inputs (internal & external) - Quantification approaches - Distribution choices - Aggregation methods and calculation techniques - Capital Allocation - Validation framework - Validation techniques

8 Challenges of current approaches to operational risk modelling Standard Formula Simple formula based on volume of business Not risk-based and lack of sensitivity to the firm s operational risk management approach Internal Model Data-driven approaches Not applicable on internal data only, due to scarcity External events: emerging trends, still scaling issues, data pool is growing Use of scenarios: the challenge of uncertainty Scenario-driven approaches Very broad range of events: how to consider them all? How to assess potential major events scenarios? How to correlate scenarios? Hybrid Approaches Where to strike the balance between the two types of input? Data driven Scenarios

9 Choice of modelling approaches The variety of approaches reflects the heterogeneity of the insurance market and the choices firms have made to produce the best model for their firm in the absence of any overly-prescriptive regulatory guidance. But there is scope for industry convergence on the use of some techniques as part of the overall process e.g. definitions of a scenario, correlations and aggregation techniques.

10 What is being modelled? There are varying approaches to what is actually modelled within an internal model for operational risk. Examples of different granularities used in the modelling process include: Risk Categories Level 1 & 2 from a recognised taxonomy e.g. ORIC International Taxonomy Bespoke internal categorisation mapped back to one of those recognised taxonomies Scenario level quantification Individual risk quantification Risk Driver quantification Must be aware of impact of chosen granularity on ensuring appropriate coverage of all relevant risks and on correlation methods.

11 Correlations This has been an area of keen regulatory focus throughout the stages of IMAP so far. Choices made can have a significant impact on the diversification benefit gained when quantifying your operational risk capital. There is currently limited statistical evidence available to inform correlation coefficient choices within operational risk. As such there is still substantial reliance on Expert Judgement to attempt to quantify these relationships

12 Quantification techniques and Aggregation 63% of respondents are modelling frequency and severity separately, of which: 86% are using Poisson distribution of frequency 73% use Lognormal distribution for severity 40% use more than one frequency distribution, 47% use multiple severity distributions Gaussian Copula is the most popular aggregation choice, but only accounted for 30% of responses. Outside of the top three (shown to the right), other options include Simple Sum, Sum of Squares and Rank Copula. 78% are using Monte Carlo simulation to generate outputs.

13 Documentation Some key points to consider: An early cross-reference to the key regulations that each document seeks to comply with can be helpful, as can schedules of related material. Structure documents in a logical way, such that a knowledgeable third party can form an understanding of your firm s structure, lines of business, and approach to modelling operational risk. Material differences between areas should be explained and justified. Explain how the capital model is a good fit for your business model. Discuss and explain alternative approaches considered and why they were rejected in favour of the approach adopted. Reliance on expert judgement is material for operational risk, so needs to be thoroughly explained and justified. This material cannot be written to sit on a shelf and gather dust it should be reviewed for currency and refreshed on a regular basis.

14 Expert judgement This means that firms have to be particularly vigilant about their evidence and documentation for areas where there is a heavy reliance on expert judgement. Examples of information used as evidence include: Minutes of workshops Participant knowledge/skills description CV s of employees providing expert judgement to document suitability Not including the risk owners in workshops to get impartial views Sensitivity analysis on judgements to assess impact of using a different assumption

15 Capital outputs The mean value for operational risk capital of internal model firms was 8%, and a median value of 7%. By comparison the mean value for Standard Formula respondents was 17%, and the median value was 16%. However there was a much smaller population size for capital figures from those firms.

16 Validation approaches 86% of respondents stated they were validating their model on at least an annual basis. Primary validation techniques used include: Key technical tests are required to ensure the robustness of the chosen modelling approach and that the output is appropriate. Examples include: Skewness Kurtosis Mean-excess function

17 Where do we go from here? The months leading up to 1 January 2016 are going to be vitally important for firms pursuing approval for their internal model. With the decision regarding approval due to be communicated to firms in December 2015, there are areas of focus that should be focussed on between now and the end of the year. ORIC International is concentrating its efforts in the second half of the year on: Scenario Benchmarking Correlations ORIC International is about to commence a project in partnership with Oliver Wyman to examine operational risk correlation, and correlations between operational risks and financial risks based on our consortium data.

18 Questions?

19 A topic for discussion: Will there ever be a time when we can implement a purely data-driven/statistical approach to the modelling of operational risk? Would we ever want to follow that route?

20 Caroline Coombe Chief Executive ORIC International Simon Cartlidge Head of Operational Risk Governance Legal & General