everis FRTB Conference Paper Feb17

Copyright © 2017 everisconsultancy Limited. 1
FRTB: Charting a course in
uncertain waters
everis was Gold Sponsor of the Marcus Evans Conference ‘4
th
Edition: Impact of the
Fundamental Review of the Trading Book’ at Canary Wharf, London on 23-24
th
February 2017.
We are embarking on
complex
implementation
projects but critical
aspects of the rules
are not yet finalised
This was a timely opportunity to catch up with banks and solution partners as we
move into the implementation phase of Fundamental Review of the Trading Book
(FRTB) programmes. We heard views and case studies across a range of topics
including market risk methodology, operating model definition and data and systems
architecture design.
Our overriding impression is that while most banks are making steady progress in
interpreting the rules and developing a strategy, there is still considerable
uncertainty that needs to be resolved before a definitive approach can be agreed
and implemented. In particular, some critical definitions remain ambiguous,
especially in view of divergences between the Basel Committee on Banking
Supervision (BCBS) text and the EU’s implementation. Hopefully, forthcoming
Regulatory Technical Standards and BCBS pronouncements will address these grey
areas.
In the meantime, banks continue to explore the potential capital impact of the FRTB
Standardised and Internal Models approaches, the implications of modelability
requirements and the optimum configuration of their trading desks. The P&L
attribution process is reported to be challenging, and with continued uncertainty
over whether and how a capital floor will be applied to banks using Internal Model
Approach (IMA), the cost-benefit analysis of seeking IMA authorisation remains
unresolved for many institutions. However, our expectation is that the majority of
banks will seek at least partial authorisation for their internal models.
Banks are modelling
the capital impact of
FRTB to evaluate the
costs and benefits of
alternative
approaches
At everis, we currently have more than one hundred consultants engaged on clients’
FRTB projects across a wide range of activities including programme management,
supporting the modelling of capital impact and business model definition, specifying
and implementing target operating models, and defining the data and systems
architecture and components required to address the full scope of FRTB impacts.
Our presentation at the conference focused on the architectural challenges posed by
FRTB. A recent estimate from Celent forecasts IT spend associated with FRTB to fall
in a range from $60 to $150 million over three years for a large bank, with peak
spend just around the corner in 2018.

In fact, FRTB requires a reassessment of fundamental aspects of the configuration of
the business, the operating model that supports it and, in turn, the data and systems
architecture that supports the operating model. This makes the effective
governance, both in terms of the implementation process and of the functioning of
the target business model, critical to success.
We see three distinct approaches that banks are adopting: (i) base compliance, (ii)
establishment of a more sophisticated operating model for FRTB based on strong
governance and analytical capabilities and (iii) a much more ambitious, holistic
approach to dynamic capital management.
The scope and impact
of FRTB presents an
opportunity to
reconsider the
approach to capital
management
The primary objective is, of course, to achieve FRTB compliance. Standardised
Approach (SA) itself is of course non-trivial, particularly in terms of the
implementation of new market risk capital calculations and reporting functions and
the complexity of deployment across different regional entities for large
international banks.
Beyond SA, banks need to determine the optimum trading desk structure and
evaluate the relative costs and benefits of IMA. Where internal models authorisation
is sought, then of course there is a wider set of requirements across data, risk capital
calculation, P&L attribution and reporting to tackle if approval at the granular level
imposed by FRTB is to be secured.
The complexity of FRTB presents additional challenges over and above basic
compliance however. The explicit risk of incurring a substantial step in capital in the
event of a P&L test failure places significant demands on banks’ ability to source and
manage data, align Front Office and risk models and dynamically monitor and
analyse FRTB outputs.

We therefore see a second level of objectives, where banks seek to put in place the
analytical infrastructure required to be able to decompose FRTB outputs and to
monitor and anticipate P&L divergences, proximity to capital floors and so on to
ensure that capital is allocated efficiently and mitigate the risk of regression to
punitive SA calculations.
Finally, with FRTB as but one aspect of the evolving capital and regulatory regime,
some banks are taking the opportunity presented by FRTB to define and implement
enhanced operating models that can support a much more comprehensive approach
to active capital management, developing what-if analysis tools to estimate the
capital impact associated with proposed trades and optimise activity across trading
desks and counterparties in order to maximise return on capital.
Whatever objectives
are chosen, the target
business model must
retain a degree of
flexibility
Many factors will determine which strategy a particular bank will adopt. The current
and expected composition of the product portfolio, the scope and scale of the bank’s
activities and the capacity and the commitment to enhance or replace legacy process
and infrastructure will all feed into the cost-benefit analysis that determines how
priorities are set.
Critically though, the target business model may need to flex to reflect adjustments
to trading desks moving in or out of scope for IMA, refinements to the FRTB rules,
the response of competitors in terms of their product strategies, the impact on the
business of parallel regulatory initiatives such as MIFID2 and so on. While the
selected architecture strategy clearly must focus on FRTB compliance, banks should
try to retain sufficient flexibility to be able to adapt to changing constraints and
opportunities.
Based on our experience gained in FRTB projects to date, and on the findings of a
survey into FRTB approaches carried out with our partner firm InteDelta, we have
developed an understanding of the challenges involved in executing each strategy,
and in particular in the definition and implementation of the data and systems
architecture required to support a chosen operating model.
Specifically from an architecture and implementation perspective, we see three main
challenges:
1. How to specify and implement the FRTB operating model to achieve and
maintain the required alignment of Front Office and Risk functions required for
IMA;
2. How to enable the ‘industrialisation’ of the capital management function, by
which we mean the sourcing and mobilisation of data and the development of
the necessary analytical capabilities required whilst retaining sufficient freedom
to adapt to evolving regulatory and market conditions;
3. How to establish and maintain the necessary cross-functional governance for
the project and for the functioning of the target operating model.
Alignment of Front Office and Risk

FRTB requires
stronger alignment
between Front Office
and Risk in terms of
risk metrics,
revaluation models
and conventions.
While it could be a valid strategy to focus solely on SA, our expectation remains that
the vast majority of banks , having modelled out the costs and benefits in terms of
capital efficiency against cost of implementing and maintaining Internal Models,
intend to achieve at least partial authorisation for IMA.
Under FRTB, IMA authorisation must be secured and maintained at individual trading
desk level, which will be extremely demanding from an operational and therefore
architectural perspective. While some banks intend that all desks should achieve IMA
authorisation, others intend to use internal models for core desks only, with non-
core desks using Standardised Approach where the cost of implementing IMA is
unlikely to justify the capital benefit that might be realised. Still others have chosen
to prioritise SA initially and may migrate trading desks selectively to IMA over time.
FRTB specifically requires that banks seeking IMA authorisation take steps to align
the Front Office and the Risk function in terms of conventions, specification of
models, provisioning of data, production processes and so on.
The first hurdle is to secure IMA authorisation desk-by-desk, and then once
operating IMA, to avoid breaches of the P&L test and the punitive capital penalty
that is incurred under SA calculations.
The need to align
Front Office and Risk
functions is a key
driver of architectural
choices
Of course this challenge is not just a question of IT architecture. The starting point is
the establishment of a cross-functional governance structure to ensure that both the
implementation project and the subsequent business model operate in a cohesive
way.
In order to achieve a reliable alignment in terms of inputs, process and outputs,
banks must ensure that revaluation models are implemented and provisioned with
market data in a consistent way with the aim of minimising the risk of discrepancies

between the output of models generating hypothetical P&L and risk-theoretical P&L
for IMA. This drives key architectural decisions such as which risk engines will be
used, how they are to be provisioned with data and so on. Based on our experience,
everis recommends re-use of Front Office risk models where practicable, although as
we heard during the conference, some enhancement and adaptation is likely to be
necessary, including the provisioning of additional reference and market data.
The starting point for many banks is often siloed Front Office systems, a mix of in-
house build and point vendor solutions that have evolved to meet particular product
requirements, each provisioned separately with reference and market data, weak
alignment between the models and data used by the Front Office and risk function
and patchy integration across the architecture. This is often more acute in banks
with operations in multiple regions and jurisdictions, where particular local
requirements and preferences have been embedded in the as-is architecture.
Some banks have
chosen to consolidate
on a Front-to-Risk
platform…
In order to minimise the potential for discrepancies between results generated in the
Front Office and Risk, some banks are contemplating consolidation on a single
‘Front-to-Risk’ platform to support SA and IMA functionality.
The benefits to be gained from such an approach are consistency of model
implementation, architectural simplification and rationalisation of data provision.
The potential trade-off that has to be made for these benefits could be the flexibility
and extensibility of the solution in the event that new requirements arise in the
future.
The ‘Front-to-Risk’ approach implies selection of a solution that will support the
required Front Office revaluation models as well as the calculation and aggregation
processes for SA and IMA outputs, and, in some measure, market data management
functionality as well.
Almost half of the participants in the InteDelta survey had selected or were
contemplating the implementation of a completely new system, this being
particularly the case for smaller banks seeking to update their architecture, achieve
the desired consistency of approach and take advantage of vendors’ FRTB
functionality. The alternative is major enhancements to existing infrastructure,
potentially combined with the selection and integration of new components to
address specific functional requirements.
We are working with banks that have chosen to standardise their Front Office and
Risk platform using a single vendor solution, Murex. This includes both a
consolidation of Front Office functionality across product groups onto the chosen
solution as well as the implementation of the incremental FRTB functionality to
support SA (initially) and IMA (subsequently). Variously this has entailed an
incremental deployment or upgrades to the current version of the solution.
This results in a complex IT transformation program including selection and
integration of the core platform, decommissioning of legacy Front Office and risk
management systems and a substantial re-engineering of upstream and downstream
integration to increase straight-through processing and reduce on-going operating

risks and costs.
…but such an
approach may be
impractical for some
institutions
Of course most banks face constraints that guide their architectural choices and that
may render such a comprehensive front-to-risk approach unachievable. These
include:
 Product coverage and regional/jurisdictional scope;
 Range of Front Office and Risk systems in use and appetite for consolidation;
 Capacity to evolve existing functional components to meet FRTB requirements;
 Existing degree of alignment of Front Office and Risk models;
 Degree of centralised control over market and reference data provision.
In view of these constraints on centralisation, banks may focus on partial
consolidation of Front Office systems and models where practicable, and seek to
improve the degree of integration between the residual discrete functional
components. Where a bank persists with different solutions in Front Office and Risk,
the importance and overhead of tight and effective governance over valuation
models and the associated data inputs is commensurately higher.
FRTB is extremely
demanding in terms
of the intensity of
data and analytical
processes
‘Industrialising’ the capital management function
It is clear that FRTB leads to several architectural challenges; the volume of data to
be sourced and processed, the implementation of new risk calculations, P&L
attribution and reporting processes, the governance and operational overhead of
achieving and maintaining IMA authorisation across multiple desks, the complexity
of analytical processes required to maintain control over the post-FRTB market risk
process and, finally, the flexibility to extend and adapt this control framework to the
wider capital management process.
The computational intensity of FRTB reflects the trend towards full revaluation and
the requirement for transparency of new risk metrics placing demanding
requirements on the risk aggregation process to deliver the necessary analytical
capabilities.
We see growing
interest in Big Data
technology solutions
as a means to address
these challenges
Here, we see a role for big data technology solutions. Big data technologies are
particularly good at consolidating data from a range of sources and improving
performance and scalability, essential in the evolution of the flexible and performant
architecture required to meet FRTB obligations in which the sharing of data is to be
promoted. Taken further, a suitably specified aggregation and analytical solution can
be the foundation for the more ambitious goal of dynamic capital management.
Early adoption of Big Data technologies in financial services has typically been
directed towards applications such as dynamic product marketing and operational
monitoring of complex infrastructures in retail banking and payments contexts.
everis has supported several such projects from our Big Data and Advanced Analytics
Centre of Excellence through which we manage our Big Data technology partnerships
and deliver design and integration services.

We are now seeing accelerating adoption of Big Data tools and practices in
institutional banking as well, particularly for complex data aggregation and analytical
tasks and also as a means of rationalising and consolidating the various solutions
developed to meet post-crisis regulatory reporting obligations, improving data
integrity, increasing interoperability, dramatically shortening production times from
days to hours and significantly reducing total cost of ownership.
The motivation to consider a Big Data approach is to be able to source and
consolidate very large volumes of data from a wide range of sources, to provide
enhanced analytical capabilities, to be able to respond flexibly to demand for new
applications and to be able to decommission legacy data infrastructure.
Big Data solutions can
be combined to meet
performance,
scalability and
flexibility objectives
for FRTB
A Big Data approach can therefore address the FRTB challenge of sourcing and
consolidating the large volumes of data which are then available for rapid
aggregation and processing using in-memory analytical solutions. In this of course we
are guided by the principles set out in BCBS 239 Risk Data Aggregation and Risk
Reporting.
In our Big Data implementations we have integrated parallel processing technologies
such as Impala and Spark for to implement bespoke in-memory analytical processing
functionality, and there are of course vendors with products tailored to institutional
banking requirements such as ActiveViam and the Murex Enterprise Market Risk
solution. These tools, combined with suitable visualisation capabilities, support the
analytical tasks required to calculate and analyse FRTB outputs, monitor and
decompose P&L discrepancies and model and track capital consumption.
To give an example, we have worked with a bank that has very heterogeneous Front
Office estate, a natural reflection of its operations across multiple regions and
products. In this case, a front-to-risk solution was found to be impractical, and a
different approach was required to meet FRTB obligations whilst retaining the
sought after flexibility.
A target architecture has been defined that features a central repository for the P&L
vectors produced by the numerous Front Office models. This data lake will utilise big
data technology to ensure performance and scalability for the very large volumes of
data that will be generated. Aggregation of the intermediate P&L vectors and
calculation of FRTB risk metrics for SA and IMA will be performed using an in-
memory analytical tools, again with an emphasis on performance and the capacity to
scale.
Such an architecture
can be extended to
support a much
broader concept of
dynamic capital
management
The Big Data approach to aggregation can help address a much broader set of
requirements than just FRTB. A sound target architecture based on a modular
structure with well-defined and uncoupled components provides the technical
capabilities and scalability to support additional functional use cases. It can be
readily extended to accommodate new data sources and analytical tasks, meeting
the requirement for flexibility as individual institutions and the industry in aggregate
adapt to the new regime.

An architecture can therefore be defined to meet specific FRTB requirements and
then extended to embed dynamic capital management with what-if and back-testing
tools to assess the capital cost of trading strategies, to evaluate the capital impact of
moving desks between SA and IMA or to model the benefit of moving portfolios
across trading desks, taking into account not only market risk but the full scope of
the Basel Capital framework, stress-testing obligations and the wider regime of
interconnected bank regulations.
Strong cross-
functional governance
and stakeholder
engagement is
essential for success
Governance
The third challenge we identified is how to establish and maintain the required
degree of cross-functional governance for the project and for the functioning of the
target business model. Our experience suggests that this is critical for the successful
delivery of an FRTB programme’s objectives, and to ensure that the architecture
choices made are appropriate.
The cross-functional nature of FRTB is obviously apparent, reflecting the scope of
activities that need to be absorbed into the bank’s operations and the complexity of
changes to organisation, process and technology.
A top-down approach and robust cross-functional governance with adequate
stakeholder representation is essential to control the complex definition and delivery
of FRTB solutions. In particular, collaboration between Front Office, risk, finance and
technology stakeholders is essential to ensure that the to-be architecture strikes an
appropriate balance where trade-offs are necessary and meets the full range of
requirements as comprehensively as possible.

This starts with a definition of the business model, where fundamental strategic
questions must be addressed. Here we consider the issues such as risk appetite,
capital allocation, trading desk structure, SA versus IMA cost-benefit analysis and
revaluation model strategy that will enable priorities within the delivery programme
to be set.
An early assessment of capital impact at the level of every desk and strategy both
under SA and IMA is recommended to establish business priorities and identify the
critical path of the whole transformation project.
Once strategic direction has been determined, the Target Operating Model (TOM)
can be specified in detail. The TOM should define all aspects of organisational
structure, new and enhanced processes, controls, validations and functional
interfaces required to support the business model.
Finally comes the definition and integration of the technology architecture that will
support the TOM. We emphasise that the ability to maintain flexibility is key; the
post-FRTB target business model is almost certain to have to adapt as regulatory
obligations are clarified and the wider market adapts to the evolving regime, and
therefore the FRTB programme must be able to support feedback loops and respond
to incremental requirements. This is something we have observed time and again as
firms absorbed the challenges of, for example, EMIR and MIFID. This need for
flexibility must inform IT architecture choices to ensure that we do not unduly
constrain freedom of manoeuvre.
Summary
The specific challenges of FRTB may be addressed by the establishment of a cross-

functional governance framework and by evolving or replacing functional
components within a bank’s front office and risk architecture.
We can consider consolidation on a ‘front-to-risk’ solution, but bear in mind the
trade-off between the benefits of consolidation and flexibility.
Where Front-to-Risk consolidation is not practicable, we can define an aggregation
solution that enables the re-use of components and delivers the required analytical
capabilities to calculate and monitor FRTB outputs at a sufficient level of
performance.
A Big Data approach can address the computational intensity and complexity
associated with FRTB and can be readily extended to accommodate new data
sources and analytical tasks.
Such an architecture can be defined to meet specific FRTB requirements and then
extended to be the foundation of a true dynamic capital management process.
Effective cross-functional governance is critical for the successful delivery of an FRTB
programme’s objectives, and to ensure that the architecture choices made are
appropriate.
About everis
everis is a multi-national business and technology consulting firm with 18,000
professionals world-wide, and is a member of the NTT DATA Group, a leading global
provider of IT services.
The everis Banking Practice provides business advisory, solution integration and
outsourcing services to banking, asset management and financial market
infrastructure institutions.
We combine our advisory Governance, Risk & Compliance (GRC) services, on-shore
and near-shore Global Banking and Markets (GBM) strategic solution vendor
integration and innovative Digital Banking capabilities to deliver comprehensive
support to our clients as they address regulatory obligations and business challenges.
For more information please contact Jonathan Philp on +44 20 7299 7890 or at
jonathan.philp@everis.com, or visit www.everis.com

everis FRTB Conference Paper Feb17

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