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 processesThe 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.

http://www.bis.org/bcbs/publ/d352.pdf

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. Super

viso

ry

Appro

vals

5. D

ata

and

Tech

nolo

gy

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. Funct

ional

Req

uirem

ents

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

GIRRCSR – Non-

Securitization

CSR –

Securitization

(CTP)

CSR –

Securitization

(Non-CTP)

Equity Commodity FX

DeltaIndividual

currency16 16 25 11 11

Individual

currency

VegaIndividual

currency16 16 25 11 11

Individual

currency

CurvatureIndividual

currency16 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

DimensionBenefits

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

DimensionBenefits

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

DimensionBenefits

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.


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

DimensionBenefits

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.


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 Interpretation1

23

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

ValueMeasurement

ProgramControl and

Administration

StakeholderManagement

ResourceManagement

Delivery Management

Quality Management

Value Management

Program Delivery

Stakeholder Acceptance

Plan MobilizePrioritize

Leadership and Governance

Manage

ValueMeasurement

ProgramControl and

Administration

StakeholderManagement

ResourceManagement

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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