Welcome.
I'm Peter Plochan, and I work as Risk Management Specialist
within SAS.
In the next few minutes, I would like
to show you how SAS model risk management can
help your financial institution to deal with the current
and even more importantly with the upcoming
challenges in the model governance and model risk
management space.
Let's get started.
When we look at what is currently
happening in the banking industry,
we can clearly see that the banks
are being challenged on multiple fronts simultaneously.
First, there's an increasing demand
for more model governance and model risk awareness.
And this is coming from both the business side,
pushing for high utilization of models
in banks' decision-taking, but also
from the regulatory side where the recent guidelines put banks
under unprecedential scrutiny.
At the same time, the banks are pushed
to faster model deployment and model performance.
And lastly, in peril, the banks are
required to build new models with a very good example being
the IFRS 9 impairments.
According to some senior model risk managers,
IFRS 9 will have significant impact on bank's MRM processes.
Let's explore a bit this impacted IFRS 9 will
have on bank's MRM processes.
Compared to the current impairment regime,
IFRS 9 will have more financial impact,
will require more impairments, and these
will have to be recalculate more often to capture the changes
in the credit quality.
The impairments calculation will be more complex
and will require a collection of more data
and development of more models.
IFRS 9 principles offer more alternative interpretation
that will define the methodology behind these new models.
All this work will require much more internal co-operation
between risk and finance.
And everything will have to be accepted
by the statutory auditor.
Therefore, robust governance around all these new data--
models, interactions, and calculations-- will be crucial.
To conclude, impact of IFRS 9 should not
be underestimated when thinking about the future of banks,
model risk management, and model governance processes.
But IFRS 9 is just one piece of a puzzle
in all these developments.
At SAS, we design our model risk management solution
that it can address not only the current challenges
that your bank is facing now, but also the ones
that are for sure yet to come.
So what is it actually?
In a nutshell, you can think of our solution
as a robust model information system that
tracks all the relevant information and stakeholder
involvement across the whole end-to-end modeling lifecycle.
It allows to establish linkages between the relevant components
of the modeling universe, for example,
between model feeding another model
or between model and their usages.
All interactions between various stakeholders and approval cycle
follow the underlying workflow to define
which information has to be provided
or which action has to be taken by whom and when.
Once all this information is collected and stored
in a secured fashion, various reports
can be easily built using the intuitive self-service
reporting engine.
All the reports, workflows, and screens or data definitions
are fully configurable by the business user
without the need for IT involvement
or any hard-code coding.
To get you started quickly, we provide out-of-the-box content
based on the industry best practices,
which you can start using, learn what works
for your organization and what not,
and evolve with as you go ahead so it best fits your need.
By having such information system in place,
you can quickly identify areas with concentration
of model risk and focus your attention there.
By following predefined workflows,
you can streamline the processes,
reduce manual handovers, but also
achieve efficiencies by sharing information
among the relevant stakeholders.
And last, but not least, be ready for any questions
about your models that might come
from external or internal stakeholders.
Let me show you now how this can look in reality.
Now, let's assume that I am a model governance
officer who wants to manage and monitor model risk.
First, I need to know where does the model risk reside
in my organization and what is driving it.
For this purpose, I can use our model risk dashboard.
This dashboard uses as an example model risk
quantification framework where we score every model
on their importance or materiality, in other words,
and quality or criticality.
By combining these two scores, we
can plot each model on a Model Risk Matrix, which
allows me to quickly spot not only
the models that are most important for our organization,
in other words, having the highest
materiality, but also the ones that require
our immediate attention.
I mean the highest criticality.
For example, I can see here that I
have 39 models that have both highest materiality and highest
criticality scores.
This is where I need to focus.
Furthermore, by aggregating these two scores, first,
on individual model level, and then
across relevant models of portfolios,
allows me to monitor model risk concentrations.
For example, I can clearly see that my derivatives model's
portfolio requires immediate attention.
I can easily zoom in to a particular business unit
and explore the model of risk concentration there.
Here, I see that within my Sunshine Financial Business
Unit, it is the inflation model portfolio that
requires our attention.
Besides the model risk quantification information,
there is much more other useful information
tracked in our system, which is available to support the bank's
governance processes.
Let's assume now that I am the manager of the model validation
team who wants to plan model validations
for the next half of a year.
In order to do that, I want to get an idea of how many models
are being developed at the moment
because these will need to get validated by my team.
For this, I will move now to the Model Governance Overview
section of our dashboard.
In this overview, I can see distribution of all models
into stages of their lifecycle.
I can also see the number of registered findings
from previous validations and what
could concern me here is the unusually high number
of pending findings, which have not yet
been accepted by the owners.
For now, I want to focus on the new models that
are going to be developed.
I see there are 34 such models.
Let's say that I am in particular interested
in scorecard models because they require
most effort to get validated.
And I see that there are 18 of them.
I want to get a better idea of what kind of models these are.
And therefore, I zoom into our model inventory.
In this section, I can now see the selected scorecard models
alongside with some key information
such as model rescore that we discussed earlier.
I see that this one model has 20 sub-models associated with it.
But it's still marked as low risk.
Let's say, for example, that I believe
that such model with that many linked sub-models
should get a higher criticality rating.
To do this, I will first zoom into the model detail section
where I can see more information about this particular model,
like associated findings, action plans, etc.
Here, by clicking on the model, I
jump into the operational environment
where the model information is managed.
Now, we will have a quick look at the environment
where we can view, review, edit the information
about our selected models, depending
on our rights and the stage of the model lifecycle.
Here, we can see that our selected model is currently
in the created stage.
We can see the description of the type of the model
and of the model itself and also, the key stakeholders
linked with this model.
Lastly, we can also see the rating information, which
I will now change to major.
This environment allows us to store and track
various information about our model such as link models
with various levels of relationship.
Here we can see the 20 sub-models.
We can also link our model with other objects,
like findings and model reviews, but also
store model documentation or model performance reports.
Depending on my role and stage of the lifecycle,
the underlying workflow defines my available actions.
And I will receive notifications when I am expected
to perform a particular task.
I can see any time where I am within the workflow.
At this point, my options are either
to send the model form to model risk management team
or to model owner.
All that we have seen here-- workflows, reports,
page definitions, and metadata definitions--
is fully configurable by a business user
without the need to do any coding.
To get you started, we provide you with out-of-the-box content
based on the industry best practices,
which you can adapt to your needs along the way.
So this was a quick overview of our model risk management
solution where I showed you how we can help your institution
not only to decrease the model risk but also reduce
the workload and increase efficiencies and awareness
and lastly, but not least, comply with regulations.
For more information, please visit our Model Risk Management
web page.
In case you have any questions, please
do not hesitate to contact me.
I would like to thank you for your time
today and have a great day.