The ongoing trend in using quantitative models for decision making prompted institutions and regulatory authorities to establish stricter rules on model risk management. Examples of this continuing expansion can be found in various model using areas e.g. the use of algorithms for trade execution in Securities Trading, the use of decision models in Credit Risk Analytics. Financial risk areas like Credit, Market or Compliance Risk entail model use cases with exposure to Model Risk.
Disclaimer: Data, charts and commentary displayed herein are for information purposes only and do not provide any consulting advice. No information provided in this documentation shall give rise to any liability of Auriscon HK Ltd and Auriscon Ltd.
Our Aproach
is to advise and support our clients in audit planning and with subject matter expertise in audit testing to ensure their objectives are realized in time. This support ultimately adds value for our clients, to help them to manoeuvre successfully a rapidly changing regulatory and competitive environment that is imposing challenges onto the operation, compliance and strategy of their businesses.
Planning
We support the planning and executing of Model Risk reviewing and audit examinations including Data Governance, Credit Risk and Traded Risk.
Specialisation
Given our specialization in Model Risk, we can suport with relevant contributions. We are a specialist provider in the model risk technical audit field and draw insight from hands-on audit experience across multiple functional and asset-class areas.
Anticipation
Our consuiltancy ams to identify latent and emerging risks to higlight issues at an early stage in the review. Detrimental impacts of emerging risks will be highlighted and explained in relation to the context.
Approach
Our approach is risk-based detail assessment augmented by holistic viewpoints.
Contact us to request further details on our support.
→ Contact
Common Root Causes for Model Risk
| Wrong Model Use | Wrong model usage leading to shortfall in governance. |
| Model Limitations | Model design limitations due to unsuitable methodologies and mismatched assumptions leading to unsuitable model outputs. |
| Data Limitations | Lack of data quallity leading to calibration bias. |
Expectations and Model Risk
| Regulatory Expections | Regulatory and rating agencies' preference for Through-The-Cycle (TTC) Credit Risk models with the caveat that models notoriously slow in reacting to sharp recessionary market downswings. |
| Business Expectations | Lending businesses often show preference for TTC models given loss outcome is less than predicted in boom cycle periods, which seemingly re-confirms model conservatism as long as not contradicted by loss outcime in bust periods. |
Examples for Model Risk
| Expected Shortfall |
Replacement of VaR by Expected Shortfall (ES) for Market Risk measurements in Traded Risk and Asset Management is accompanied by technical challenges. Validation and backtesting of ES with unsuitable methods is leading to Model Risk . |
| Machine Learning | AI-ML methods such as boosted trees used in transaction fraud and credit risk requires suitable KPI for monitoring interpretabiliy. Lack of transparency of risk driver used in black-box methods lead to interpretation gaps and heightened Model Risk. |
Review and Audit Phases and Activities
Audit activities occur in phases, with each phase consisting of specific tasks for preparing and inputting any subsequent phase. Of particular importance is the initial phase also known as Audit Planning. The planning phase is used to communicate the scope of the audit to auditees and management and to identify the areas of inherent and emerging risks.
Outcome and Reporting
Our approach to reviewing and technical auditing ensures that controls marked as ‘critical’ receive prioritization during audit fieldwork and assessment.
We perform auditing through colloboration involving auditees and senior management to ensure audit tesitng is effective throughout.
The reporting of the Audit outcome will be clearly communicated and based on a consistent documentation.
Thematic drivers of issues will be identified and presented together with contextual information.
Mapping of Asset Class and Risk Areas to Regulations
Regulators have confirmed that financial institutions must implement a Model Risk Management (MRM) framework.
This includes setting up adequate governance to cover risk models with policy details for model life cycle.
In addition, periodical follow-up via reviewing and validatoins and management assessments as set-out in the model risk managment policy.
