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This post summarises the key messages and points from the webinar – Cloud in Financial Services: The application of Reputational Risk Q-model, a spin off from the ten-part Around the Cloud webinar series.
Panel members include Antonio Bucci, Chief Risk Officer at Banca Generali, the first Reply client to adopt this model, Paolo Fabris, Partner at Avantage Reply, Gianluca Nieri, Senior Solutions Architect at Amazon Web Services (AWS), Stefano Bussolino, Partner at Storm Reply, and Frederic Gielen, Executive Partner at Avantage Reply.
The Reputational Risk Q-Model marks a shift from defining, articulating and managing reputational risk in a purely qualitative way to quantifying the risk in a consistent way with the other main risk types financial institutions are exposed to.
Paolo explained that the development of such a quantitative model is necessary as the expectations of regulators are evolving in this space, with a more fact-based assessment expected alongside the qualitative approach that has historically existed in isolation.
Antonio explained how quantifying reputational risk enables financial institutions to articulate a more tangible risk appetite statement and to monitor and manage risk more effectively. Senior management and Board understanding of the risk and how it is evolving will be improved and it informs better risk management and strategic decision making.
Paolo explained the different components of the Q-Model and how it works. The basis of an institution’s reputation, and by extension its reputational risk profile, is how other relevant stakeholders perceive it. There are ten stakeholders within scope of the model, however, not all are relevant for each institution depending on business model and operations. The full set of stakeholders are: 1. Shareholders 2. Bondholders 3. Clients 4. Market Counterparties 5. Suppliers of Goods and Services6. Employees 7. Financial Advisors 8. Supervisory Authorities 9. Community and Local Authorities 10. The Media
At the centre of the model sits an engine fed by a set of standardised rules while thresholds and parameters are used to customise the engine for a specific institution. While the rules can be standardised, the thresholds and parameters will be different reflecting the heterogeneity of firms. Data is fed into the engine with the quantification of reputational risk being the output.
Within the model there are four levels of granularity: 1. Trust and Reputation Synthetic Index 2. Stakeholders 3. Risk Factors 4. Risk Indicators
Users of the model can drill down into each to understand for example how different stakeholders are contributing to the overall score, which risk factors are driving this contribution, and how related risk indicators are changing over time.
Not only does the Q-Model provide a comprehensive and granular view of risk indicators and risk factors across stakeholders impacting an institution’s current reputational risk position, it includes the functionality to conduct sensitivity analysis and run simulations in which these risk indicators and risk factors are different.
Overall, this quantitative view of an institution’s reputational risk position, both current and forward looking, provides senior management and the Board with the tools required to effectively articulate the reputational risk appetite and monitor and manage within that.
Stefano described the challenges in running the Reputational Risk Q-Model as it ingests a vast amount of data and this data feeds into a number of calculations. As a result, basic storage and calculation approaches such as Microsoft Excel are not feasible. Given this is the first exercise of its type, there were no existing products that could be employed.
Stefano explained that the Q-Model uses a Software as a Service (SaaS) offering provided by AWS to house the data and carry out the calculations. This approach is flexible in terms of the amount of data that can be added to the platform and when, and it is a serverless model meaning no maintenance is required. The user interface is a web application based on a number of best in class OpenSource frameworks.
Gianluca set out four key features of the AWS offering:1. Agility – users can scale up services, deploy workloads and role out new applications in a timely manner. If a service fails, they can be deprovisioned without any risk. 2. Scalability – In some cases, there can be an overprovision to ensure operations can be managed when there is a high level of activity. With AWS SaaS, users can provision the amount actually needed knowing they can scale up or down according to business needs. 3. Cost Effective – The pay as you go model allows users to manage expenses in line with needs, only paying for services as they are consumed. AWS variable costs are lower than what users can do for themselves due to economies of scale.4. Region Advantages – AWS has secure, extensive and reliable infrastructure across 24 regions with 76 availability zones.
Security is an important consideration in the context of the Q-Model and the data it consumes. Gianluca explained how AWS has a 24/7 monitoring system in place to protect user content and provides tools customers can use, for example to encrypt data. Users can also choose from a selection of third-party security solutions. Utilising this AWS SaaS for the Q-Model allows users to focus on the data and analyses involved rather than the underlying infrastructure and maintenance.
If you have any questions about any of the above, please feel free to reach out to us at Reply at .