Fundamental Review of the Trading Book (FRTB) – Data Challenges

Fundamental Review of the Trading Book
(FRTB) – Data Challenges

Agenda
2Copyright © 2016 Accenture All rights reserved.
Background
Data Challenges
Remediation Measures
How Accenture Can Help

Background

4
The new framework proposed by the Basel Committee on Banking
Supervision (BCBS) is in our view an improvement to the existing
market risk management processes
The revised framework for market risk capital requirements, also known as the Fundamental Review of the
Trading Book (FRTB) during the consultative phase, seeks to remove the weaknesses around risk evaluation found
in “Basel 2.5,“ by addressing the undercapitalization of the trading book.
Copyright © 2016 Accenture All rights reserved.
Trading and Banking Book Boundary
 Clear identification of trading instruments:
 Limitation in moving instruments between Regulatory Books.
 Trading desk identification with clearly defined
business/trading strategies.
Standardized Approach (SA)
 Emphasis on standardized model (SM).
 Will serve as de facto” floor for capital requirements and possible
benchmark between banks.
 Becomes more sophisticated (sensitivities based) and narrows gaps
between internal models.
 Mandatory reporting of SA results.
Internal Model Approach
 Move away from value-at-risk (VaR) towards expected shortfall.
 Introduction of non-modelable risk factors to capture residual risk.
 Use of market liquidity horizons for calculating stressed market risk
provisioning.
 Three stage approval process – from firm-wide internal risk capital
model to assessment of individual trading desks.
 Use of P&L for internal model validation.
HighlightsBCBS Proposed Changes
A. Trading
Book Boundary
B.
Standardized
Approach
C. Internal
Model
Approach
Trading Book Boundary
Internal Risk Transfer
Trading Desk Identification
Non-Modelable Risk Factors
Default Risk Charge
Covered Instruments
Residual Risk Add-On
Expected Shortfall
Sensitivities-Based Method
Profit and Loss (P&L) Attribution
Backtesting
Supervisory Approvals
Source: Minimum capital requirements for market risk, BCBS, January 2016. Access at:
http://www.bis.org/bcbs/publ/d352.pdf.

5
The compliance deadline appears to be far out in the future … but
the TIME TO ACT IS NOW…
Although the compliance deadline of December 31, 2019 seems far into the future, banks should begin their FRTB
compliance journey today in order to properly address some of these key FRTB implementation issues.
Q3 2016 – Detailed
project plan and gap
analysis complete
Q4 2016 – FRTB Project
scheduling and funding
aligned
2017 – Infrastructure
development and
facilitating technology Q1 2018 – Parallel
run of FRTB begin
Q1 – Q3 2019 –
Supervisor approvals
Dec 2019 –
Compliance
deadline
• Banks should ideally use 2016 to organize and
plan their efforts for FRTB implementation.
• We are expecting FRTB rules to lead to
significant technological and procedural
changes in the market risk management
function which would need ample lead time for
implementation as required by the rules.
Source: Minimum capital requirements for market risk, BCBS, January 2016.
Access at: http://www.bis.org/bcbs/publ/d352.pdf, and Accenture estimates.

6
Majority of FRTB rules have a direct or indirect impact on banks’
data management strategies
The new market risk framework is expected to have the highest impact on banks’ data intensive activities within
their risk management functions.
Source: Accenture Analysis. High Impact Activities
1.
Trading Book Boundary and Risk Policy
3.
Internal Model Approach
2
Standardized Approach
1.1
Trade and Bank Book
Boundaries
1.2
Trading Desk
Identification
1.6
Risk Management
Policies
1.3
Internal Risk Transfers
1.7
Reporting
Requirements
1.4
Covered Instruments
3.4
Default Risk Charge (DRC)
– IMA
3.5 Non-Modelable:
Capital Add-Ons (Stresses
Expected Shortfall)
3.1
Risk Factor Analysis
3.2
Expected Shortfall
Calculation
3.6
Multi Liquidity
Horizons
2.6
2.4
Delta, Vega and
Curvature Calculation
2.5
Default Risk Charge
(DRC) - SA
3.3
Trading Desk Eligibility
3.7 Calibration to
Stress Period
2.2
Establish Risk Classes
2.1
Sensitivity Based
Method (SBM)
2.3
Securitization
2.7 SA Capital
Calculation
Methodology
3.7 IMA Capital
Calculation
Methodology
4.Supervisory
Approvals
5.Dataand
Technology
4.1
Trading Book
Boundary
4.2
Exception For Covered
Instruments
4.3
Instrument Redesignation
5.1
Asset Classification
5.2
Security Reference Data
5.3
Instrument Master
4.8
IMA Risk Factors
4.9
Backtesting
4.10
P&L Attribution
4.11
Changes to IMA Model
5.6
Risk Factor Pricing Data
5.7
Stress Calculations
5.8
Full Revaluation
5.9
P&L Attribution and
Backtesting
5.3
Risk sensitivities data
Sourcing
5.5
Capital Aggregation
5.4
Data Taxonomy
4.4
Approval
4.6
SBM Calculator
4.7
Model Validation
1,2,3.Functional
Requirements

7
It is vital to have a set of core design principles when planning the
implementation of a strategic FRTB solution
Planning For
Compliance Now
The complexity of FRTB proposals require firms to act now due to tight
implementation timelines and large body of work, and put in place a program
to understand the overall impact to the firm from a business and technology
perspective.
Think Global
Not Local
How to execute the FRTB framework at a global scale, given the different
rules from supervisors of different jurisdictions where the organization
operates in.
Identify Strategic
Capabilities and
Synergies
Leverage existing infrastructure and programs for current regulations BCBS
239, BASEL III, Comprehensive Capital Analysis and Review (CCAR), Uncleared
Margin Rules (UMR), Capital Adequacy Requirements (CCR) etc. to identify
strategic platforms and capabilities for further investment.
Convert Regulatory
Challenges into
Opportunities
Regulatory reform should be viewed not as a threat to growth or revenues,
but as a strategic opportunity to better position the firm going forward
through further improvement of existing business as usual (BAU) efforts and
processes in managing risks.

8
By grouping the data challenges into three major categories, banks
can address their data issues in a planned manner
In our assessment, the biggest areas of impact should be the data challenges arising from the new rules.
Effectively addressing these is fundamental to the implementation of the FRTB framework and will be one of the
foundational areas of work in any bank’s FRTB program.
• The rules for SA and IMA both advocate the use of risk sensitivities and
consistency in their calculation which for the first time will be required to be the
same as used in the pricing models or instrument prices being used for the profit
and loss that is reported to management.
Risk Sensitivities
Sourcing
• Banks need to source pricing information for risk factors to be eligible for
inclusion in IMA calculation. These market prices need to be “Real” and from
observable transactions.
• Due to P&L attribution there is a greater need to align front office (FO) pricing
models and risk calculation engines which necessitate the need for consistent
data sourcing.
Market Data
Sourcing
• Understanding the incremental data requirements vs. the existing data
calculation models and calculators is crucial as FRTB has introduced changes to
the way risk charge is calculated under both SA and IMA.
Risk Calculator
Data Gaps
Access at: http://www.bis.org/bcbs/publ/d352.pdf, and Accenture analysis.

Data Challenges

FRTB rules have introduced changes to the standardized approach process, as well as tightened the norms for use
of IMA. Due to this banks should expect to face increased technological and process complexities.
• Comprehensive calculation of risk under SA.
• Previously, SA processes did not include the calculation of risk
sensitivities, therefore banks may need to develop this capability.
• Banks making use of IMA models may have been computing these
sensitivities as part of their internal models but the computation
methodology used may have differed, thus leading to changes in
the technology setup.
• Use of correlations between assets pairs for each risk class within
each of the sensitivities add to computational challenges.
• The SA has introduced the concept of curvature risk to help capture
nonlinear risk, which is not captured by the delta of the instruments
with optionality.
• Curvature risk is not a second order approximation, but rather a full
revaluation needed for every instrument affected.
• New rules mandate consistency between the calculations used for
computing sensitivities and the valuation models being used by FO
for trading purposes. Therefore synchronizing data between FO and
the Risk Office is critical.
FRTB rules should result in a quantum jump in the number of
calculations made using both SA and IMA
Copyright © 2016 Accenture All rights reserved. 10
GIRR
CSR – Non-
Securitization
CSR –
Securitization
(CTP)
CSR –
Securitization
(Non-CTP)
Equity Commodity FX
Delta
Individual
currency
16 16 25 11 11
Individual
currency
Vega
Individual
currency
16 16 25 11 11
Individual
currency
Curvature
Individual
currency
16 16 25 11 11
Individual
currency
Source: Basel Committee on Banking Supervision, 2016
Under FRTB, banks have to compute at least
79 different calculation inputs (excluding
General Interest Rate Risk (GIRR) and
Foreign Exchange (FX) risk, also assuming
that the market portfolio has assets across
the buckets) for each sensitivity class for
risk computation under SA.
Example: The new prescribed risk factors
and liquidity computation complexity may
lead to ~12,000 calculations per trade
compared to the current range of 250 – 500.
Changes From Existing Process

11
Banks should be well served if the operational and technological
challenges are provided for in the implementation plan
SA-based calculations are mandatory for all banks and some of the key operational and technological challenges
they face include:
Operational Challenges Technology Challenges
1. Maintaining consistency in FO and Risk
Management data for calculating sensitivities.
2. Having the FO Risk engine generate sensitivities
across the prescribed buckets and tenors for each
asset classes and for each risk factor.
3. Sourcing and aggregating FO sensitivities data for
all risk classes along specified buckets and tenors.
4. Capturing the value of investment in sourcing full
set of risk sensitivities for SA calculation vs.
partial set of risk sensitivities.
5. Addressing computational challenges for the
mandatory calculation of SA.
6. Maintaining consistency and common risk
taxonomy of risk factors across FO and risk
infrastructures.
1. Redesigning infrastructure to deal with FRTB
computational challenges:
a. Mandatory calculation and reporting of SA
at desk level.
b. Sourcing of a significantly increased data
set for SA calculation.
2. Assessing the right trade-off between
computational speed and the complexity/
granularity of calculation processes.

12
Banks should streamline their market data sourcing efforts to
maintain consistency in the calculation of risk metrics across the firm
With the requirement of having "Observable Real Prices," BCBS has put the onus on banks to base each of the
risk factors used in internal models on market data and not on internal bank data which may be arbitrary.
Risk Factor
Analysis
•“Real Prices” to
help identify if
risk factors are
modelable.
•24 observations
in a year.
•Pricing of illiquid
positions.
Liquidity Horizon
Management
•Differentiated
liquidity horizons
by risk class to
compute
Expected
Shortfall.
•Alignment of
liquidity horizon
buckets with
instruments
across the
trading desks.
Banking and
Trading Book Data
•Consistency of
internal ratings
between banking
and trading
books.
•Sync probability
of default (PD),
loss given
default (LGD)
and Recovery
Rates with
banking book
issuers.
Calculation
Models
•Risk sensitivities
calculated in FO
and the risk team
to use same data
sets.
•Consistency in
risk factors used
for pricing and
sensitivity
calculation.
Risk Sensitivity
Data
•Identification of
the term
structure on
which to map
the risk factors
for each
sensitivity.
•Tagging of
trading book
instruments
which are to be
included in
“Residual Risk
Add-on”
calculations.
Access at: http://www.bis.org/bcbs/publ/d352.pdf, and Accenture analysis.

External and internal data sourcing could prove to be demanding given the complexity of the technology
environment in banks and the need to have consistent data sets among different teams.
• Potential for abuse of the framework by providing uncommitted quotes which could lead to
regulatory sanctions on the entire initiative.
• Concerns of collusion between institutions which could lead to manipulation of market data
in a similar fashion as that of the LIBOR manipulation (London Interbank Offered Rate).
• Strong governance and controls, essential to preventing any misuse or manipulation of the
utility and which would pose its own set of challenges.
• Single vendor may not be able to support all external data requirement leading to increase in
complexity.
External Market Data
• Data used in FO is sometimes not consistent between different teams. Example: Treasury
curves used for pricing may be different across teams leading to different valuations.
• Lack of standardized data sources for reference data, instrument masters etc.. and which may
lead to inconsistencies in data input for models.
• Credit risk models in banking book and trading book should be consistent to align default risk
charge computations to each other.
Consistency of models
• The volume of issuers in the trading book is going to be significantly higher than those in the
banking book; resulting in cases where internal ratings are not available for issuers in trading
book.
• These internal ratings for trading book issuers should be consistent with the banking book
issuers.
• There may be instances where the banking book processes cannot assign an internal rating
for an issuer. Banks would have to prepare for these scenarios and define a process to handle
such cases.
Internal Ratings Management
Sourcing external market data and maintaining consistency of
internally sourced data is key to the implementation effort

14
With SA-based calculation being mandatory for banks, there are risk
calculator gaps which need to be considered during implementation
Maturity
Mismatch
• The FRTB rules framework defines the risk factors and vertices to calculate the sensitivities.
• These risk factors and vertices have maturities which may differ from the existing risk computation systems.
• This mismatch in maturities may cause a deviation in the calculation of risk charge under SA.
Data Sourcing
Gaps
• The existing risk infrastructure does not source/obtain all the data required for calculating the capital charge under SA
as specified in the FRTB rules.
• Data sourcing challenges exist in the decomposition of equity baskets/indices, multi underlying products
decomposition, sourcing equity rating data for default risk charge computation and managing internal ratings for both
credit and equity issuers.
Assumptions
• Due to existing data challenges in the risk process models, many assumptions have to be made by the risk management
teams which may lead to poor calculations for capital charge under SA.
• Banks may need to make assumptions for doing linear extrapolation of risk sensitivity calculations where underlying
data is not available to them. Another area is for allocation of exposures to buckets of risk factors which may be based
on certain assumptions.
Data
Taxonomy
• Due to difference in FO and risk management systems, there is a challenge in having the different products classified
and bucketed as per the FRTB rules. Consistency in calculation and uniform interpretation of the asset classes should
be a priority.
• Mapping instruments to the relevant asset classes as per FRTB rules becomes a major challenge. Creating an
instrument master will be a big driver of change in risk processes for complying with FRTB provisions.
• Inconsistent definition of risk factors and valuation methodologies across different teams should be resolved.

15
Similar to SA, IMA-based risk calculators also have data challenges
which should be accounted for during the implementation phase
Rules
Interpretation
• There are key data issues around several possible interpretations of the rules for Risk Theoretical P&L for satisfying the
P&L attribution burden for IMA.
• Upfront guidance is needed from supervisors to help avoid poor implementation of the P&L attribution models and risk
factor issues which may arise on account of “modelable or not” classification.
Data
Sourcing
• The revised IMA approval process requires data for market risk calculations as well as for developing robust testing
mechanism to obtain approval for use of internal models.
• There are multiple challenges in data sourcing for IMA models. These start with managing complex risk factor mappings
which contain different asset classes, having a clear process for “non-modelable” identification of risk factors and the
implementation and mapping of liquidity horizons for different assets classes.
Assumptions
• FRTB rules detail the process for the P&L attribution for the internal models and require full revaluation methods. Due
to high demand on computing resources, banks currently use approximation methods to simplify calculations.
• Systemic assumptions to be made for full revaluation of positions would in our view lead to auditory comments from
supervisors. Worst case scenario should lead to fall back on SA calculation in absence of hard data to back the internal
models.
Data
Taxonomy
• As stated before for SA, having a consistent data taxonomy should serve as a bedrock for all risk computation.
• In addition to the challenges listed for SA, IMA to also cater to products which are booked outside of the normal data
ecosystem which may present bespoke data challenges. This along with inadequate risk factor selection and inventory to
satisfy the audit burden of P&L attribution should help strengthen the case for data taxonomy.

Remediation Measures

In our view, for the effective implementation of an FRTB program, banks should have a sound data sourcing,
calculation and management strategy. Addressing these key data questions provides the foundation to be flexible
and agile in the FRTB compliance efforts.
#
Key
Recommendations
Analysis
Dimension
Benefits
1 Identify a consistent
set of sensitivities
Methodology
• Methodological approach for bucketing sensitivities or risk exposure for individual risk classes.
• Have consistent calculation methodologies across the bank. Ideal scenario would be that the
sensitivities are calculated only once by a golden source calculator and then utilized by
different teams as needed.
Taxonomy
• Have the same sensitivities definition across FO and risk management teams. This can be done
by having a common taxonomy for both teams.
• Have standard data taxonomies for attributes across risk classes and sensitivities and use these
throughout the organization.
• Application of sensitivities to product types in a consistent manner and across the bank.
• Consistent treatment of
data across.
• FO and risk mgmt. teams
have same calculations
and sensitivity data.
2 Define a centralized
architecture for
sourcing risk data
Data Sourcing
• Have a centralized repository for all risk sensitivities that receives data from different golden
sources for risk sensitivities and store/organize it by risk class, bucket, tenor and risk factor.
o Finalize the list of sensitivities to be sourced in the repository for each bucket across risk
classes.
o Identify golden sources of sensitivity calculation across risk classes.
o Create data sourcing standards for sensitivity data sourcing.
o Define feed formats for obtaining data for each sensitivity. Preferred practice would be to
establish a unified feed format which can be used for sourcing data from multiple sources.
This helps lead to consistent data processing for storing in the repository.
o Establish data feed service-level agreements (SLAs) and frequency with source systems for
obtaining the data. Preferred practice is to obtain the data feed daily with a pre-defined
cutoff time for global operations.
• Golden source of risk
data.
• Ease of data quality
management.
• Availability of data
across the organization
as per SLA needed.
• Support to approval
process and supervisory
auditing.
Banks can implement an FRTB solution by considering recommendations
to address data challenges posed by the new rules (1/4)

#
Key
Recommendations
Analysis
Dimension
Benefits
3 How to manage IMA
risk factors and
liquidity horizons?
Taxonomy
• Individuation of criteria and indicators for distinguishing between modelable and non-
modelable risk factors.
• Exploiting monitoring of the time series and the quality of the contribution.
Data Sourcing
• Participating in data pooling initiatives within the industry or subscribing to third-party
vendors for obtaining real prices. However, this approach has its own risks as there is a
possibility of price manipulation by industry consortium in order to skirt the regulatory
requirement and thus may be rejected by the supervisors.
• Identify data providers and establish vendor relationships to obtain real pricing information.
Data Quality
• Develop activities for the control of data for each desk instead of the Legal Entity as a whole.
Aggregation
• Structuring computations in order to more easily manage the inclusion/exclusion of the desk
considered eligible/ineligible for the IM.
• Support to individual
desk approval for IMA.
• Flexibility in switching
to SA approach in case
of rejection by
supervisors.
• Reduced capital
charges due to IMA.
4 How to plan for P&L
attribution?
Taxonomy
• Define the factors governing the portfolio which is to be considered for P&L attribution and
communication protocols to different departments involved such as Finance to help integrate
the desks which are eligible for internal model.
Governance
• Revision of report system for Risk Management on the outcome of the backtesting.
• Approval for use of IMA
to compute capital
charges.
• Successful P&L
attribution tests.

#
Key
Recommendations
Analysis
Dimension
Benefits
5 How to manage SA
risk sensitivities?
Governance
• Document existing data in FO systems which is used for risk sensitivity calculations.
Data Quality
• Periodically update the data set to help confirm the existing risk factors and identify any new
risk factors impacting the models.
• Consistent calculation
of risk sensitivities
across FO applications.
• Identification of
sensitivity gaps which
can be corrected.
• Up to date SA
calculators.
6 Where to improve
market data process
for data quality
management?
Infrastructure
• Integration of the IT processes which warn/alert the users of the data issues in the repository.
This would help with the following:
o Ability to proactively take action and the timely resolution of the issues with direct
communication toward the Risk Technology function.
Data Quality
• Signaling to both users and impacted functions the data issues and eventual delays in order to
help improve the management of the activities.
• Data quality
management.
• Efficient
communication for
reporting.

#
Key
Recommendations
Analysis
Dimension
Benefits
7 What are the
technology synergies
with other regulatory
initiatives?
Infrastructure
• Banks would do well to identify synergies with other strategic regulatory initiatives such as
BCBS 239 and UMR. To leverage the existing infrastructure for supporting FRTB or if they are
in the middle of implementation, so that the technology solutions for different regulatory
programs are supporting FRTB needs as well.
• UMR regulations proposed by BCBS in their final rules, published in December 2013 and
adopted by regulators in US, propose use of “Greeks” which are similar to the sensitivities
proposed under the SA framework for FRTB. Additionally the calculation mechanism is similar
to the one shared by FRTB.
• BCBS 239 regulations propose automated risk reporting and data traceability from source to
use of risk data.
• Identification of
strategic platforms and
technologies to invest
in.
• Avoiding duplicative
work.
• Cost savings due to
sharing of processes
and infrastructure
across multiple
programs.
• Delivering compliance
across all regulatory
regimes.

Most banks have elements in place to begin implementing their FRTB solution. They should link these elements
together to create a comprehensive approach to market risk management.
Proposed FRTB rules seek to remove the weaknesses around market
risk evaluation found in “Basel 2.5.” These rules are a comprehensive
overhaul of the market risk framework in place today
• Identify gaps using the current
state assessment and target state
definition.
• Identify areas where remediation
work is required for compliance.
• Finalize funding requirements
and make provisions.
• Identify gaps in resources and
skills.
• Finalize the technology changes
to deliver target state.
• Revisit target state and make
changes if needed.
Gap Analysis and
Implementation Strategy
• Finalize target state technology
and business operation
capabilities.
• Identify strategic platforms and
solutions to be leveraged in
target state environment.
• Define the organizational
structure for compliance.
• Participate in industry forums.
Target State Operating Model
• Perform a detailed impact analysis
of the FRTB rules on capital
requirements and processes
involved.
• Form assessment workstreams.
• Identify categories of impact and
analysis dimensions.
• Understand current capabilities
for People, Process and
Technology.
Rules Interpretation
1
2
3

How Accenture Can Help?

How Accenture can help?
Accenture has project experience in supporting FRTB programs with a select group of large global banks. Using
our investment accelerators and tools, banks can ramp up their FRTB implementation.
 Mapping of FRTB requirements to different bank
functions and teams.
 Analyze data source required for compliance.
 Develop a common and consistent internal interpretation
of what is required for new market risk framework.
1. FRTB Rule
Interpretation
Analysis
 Setup FRTB program governance standards.
 Identify implementation workstreams.
 Develop solution design.
 Analyze funding requirements and budgetary estimates
for implementation.
3. Program
Initiation
 Review and challenge of compliance activities.
 Define scope and body of work required for capital
charge calculation.
 Develop an internal point of view on activities required
for compliance.4. Data Gap Analysis
and Solution Design
 Business analysis capabilities to drive business
requirements and the analysis for FRTB implementation.
 Large body of work in application development,
integration and support.
 Project Management Office (PMO) support for managing
FRTB program workstreams.
5. Implementation
Support
 Vendor selection and strategic fit evaluation.
 Preferred vendor relationships with the major market risk
solution vendors.
 Integrated implementation of third-party solutions.6. Vendor Selection
and Product
Implementation
 Define scope and body of work for capital charge
calculation under the new methodology.
 Inform and define framework for capital calculation and
estimate the impact on a bank.
 Develop and evolve capabilities for calculating capital
estimates.
 Develop strategy for engagement with regulator(s) and
market participants.
 Trading book boundary setup.
 Technology environment readiness for transition.
 Trading desk eligibility analysis for IMA.
2. FRTB Impact
Assessment

Copyright © 2016 Accenture. All rights reserved. 24Confidential and Proprietary Information of Accenture
We would use our Accenture Managed Services methodology and the FRTB tools and assets to implement the
overall SA Capital Calculation solution.
Accenture FRTB Assets and Tools
Plan MobilizePrioritize
Leadership and Governance
Manage
Value
Measurement
Program
Control and
Administration
Stakeholder
Management
Resource
Management
Delivery Management
Quality Management
Value Management
Program Delivery
Stakeholder Acceptance
Plan MobilizePrioritize
Leadership and Governance
Manage
Value
Measurement
Program
Control and
Administration
Stakeholder
Management
Resource
Management
Delivery Management
Quality Management
Value Management
Program Delivery
Stakeholder Acceptance
Program Management
Methods
Estimating Models Strategic Delivery Model
and Alliance Network
Client Sites
Strategic Delivery Model
Onsite & Regional Global Delivery
Methodology, Tools and Architectures
Delivery
Location
Onsite delivery
Delivery Centers
India,
Philippines,
China
Multidisciplinary Workforce
Delivery Centers
Wilmington, Chicago, Atlanta,
Toronto, London, Spain
Prague, Bratislava
FRTB Rules
Interpretation Tool
Integrated Quality
Management
Are Supported by
Is Implemented by
Constrains the Process
StandardsPolicies PoliciesStandards
Training
QPI Curriculum,
Project Specific &
Accenture Core
Metrics/Tools
Tracking Tools
(Risk, Issue, Peer
Review, CR/SIR)
Process
Tailored by Project
Procedures
Defined by Project
Source:
"A Software Process Framework for
the SEI Capability Maturity Model,"
PI Liaisons
Coaching,
Mentoring,
Quality Reviews
Accenture Delivery
Methodology (ADM)
Are Supported by
Is Implemented by
Constrains the Process
StandardsPolicies PoliciesStandards
Training
QPI Curriculum,
Project Specific &
Accenture Core
Metrics/Tools
Tracking Tools
(Risk, Issue, Peer
Review, CR/SIR)
Process
Tailored by Project
Procedures
Defined by Project
Source:
"A Software Process Framework for
the SEI Capability Maturity Model,"
PI Liaisons
Coaching,
Mentoring,
Quality Reviews
Accenture Delivery
Methodology (ADM)
Accenture FRTB
Delivery Suite
FRTB Assets
and Tools
FRTB Implementation
Framework
FRTB Implementation
Methods
FRTB Implementation
Management

Fundamental Review of the Trading Book
(FRTB) – Data Challenges
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