wwwpwciebanking 22 October 2014 Agenda Background to IFRS 9 The project and timetable for implementation Classification and measurement Overview of Expected credit losses in IFRS 9 Implementation ID: 381478
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Slide1
IFRS 9 Implementation Challenges
www.pwc.ie/banking
22 October 2014Slide2
Agenda
Background to IFRS 9: The project and timetable for implementation
Classification and measurementOverview of Expected credit losses in IFRS 9Implementation ChallengesConclusionsSlide 222 October 2014IFRS 9 Implementation ChallengesSlide3
Background to IFRS 9:
The project and timetable for implementation
1Slide 322 October 2014IFRS 9 Implementation ChallengesSlide4
Effective date and transition
Overview
The effective date will be for annual periods starting on or after 1 January 2018.Retrospective application is required except:If on transition application requires undue cost or effort, operational simplifications are provided.No requirement to restate comparatives.Slide 422 October 2014IFRS 9 Implementation ChallengesSlide5
How well are banks positioned currently?
IFRS 9 - current status and emerging practice
Having established an effective date for IFRS 9, banks are taking stock on the impact of IFRS 9 and their approach to implementationEU / EFRAGEmerging PracticeIASBIASB published IFRS 9 on 24 July 2014IFRS 9 is mandatory from
1 January 2018
IFRS 9 needs to be applied in entirety, except for the OCI treatment of OCS of financial liabilities in FVOEarly application is allowed (endorsement required in the EU)
Endorsement process not yet started
EFRAG/EU are currently constituting the respective bodies
Endorsement process not expected to start before the end of 2014
Endorsement process of comprehensive standards such as IFRS 9 usually takes 12 months or longer
The level of effort to date has been mixed
. Most banks have closely followed the development of IFRS 9
Many banks, particularly in Germany, have already conducted high-level impact assessments on IFRS 9 Classification & Measurement and ECL. Many banks are now starting implementation projects.
Others are adopting a wait-and-see approach.
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IFRS 9 Implementation ChallengesSlide6
Classification and measurement
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Amortised cost
FV-PL
FV-OCI
Key question is where these lines are drawn.
Amortised cost
Hold to collect; and
Solely payments of principal and interest.
Fair value – OCI
Hold to collect and sell; and
Solely payments of principal and interest.
Fair value – P&L
Residual category.
Classification and measurement of financial assets
Overview of three categories
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Why is classification & measurement important to Expected Credit Loss determination?
Classification
under IFRS 9 for investments in debt instruments is driven by the entity’s business model for managing financial assets and their contractual cash flow characteristics.A financial asset is measured at amortised cost if both of the following criteria are met:The asset is held to collect its contractual cash flows; andThe asset’s contractual cash flows represent ‘solely payments of principal and interest’ (‘SPPI’)Key issues impacting on ECL:Reclassifications of assets and/or portfolios are highly likely to occur, as the criterial for classification & measurement are very different.A single entity can have more than one business model for managing similar financial instruments. For example, an entity can hold one portfolio of mortgages in order to collect contractual cash flows and another portfolio of mortgages (with similar characteristics) that it manages in order to sell/or to realise fair value changes.Classification changes, especially from AC to FVOCI
or FVTPL will directly impact on the determination
ECL
and thus impact regulatory capital.
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IFRS 9 Implementation ChallengesSlide9
Key challenges for IFRS 9 implementation
C&M Considerations
Definition of BM by senior management
Selling decisions with impact on accounting
Processes and systems required to document BM and reasons for sales
Use of existing BM documentation and portfolio structures as starting point
Informing SM about requirements and strategic options (e.g. on transition date)
C
hallenges
Mitigation
SPPI assessment at instrument level
Required information not available
Business units to be included
Improvement
/
implementation of systems
Clustering & use of efficient questionnaires
Training of business units
Business model
Contractual
cash flows
High quality FV needed for (structured) loans
FV needed for modified loans
May result in
P&L
and Equity volatility
Implementation of FV models for loans
Improvement of existing IT systems
Fair value measurement
Availability of
data on transition
Determining opening position impacts
FV
may be needed for loans currently at
amortised
cost
Identify data gaps and capacity of
existing IT systems
Deploy simulation tools to identify and quantify impacts
Develop, build and test FV models for loans
Transitional impacts
Reconciliation between
IAS
39 measurement and
new measurement categories under IFRS 9.
Additional qualitative
and quantitative information
is required to be
disclosed.
Need to communicate clearly to investor base.
Mock up of disclosures
Regular contact with regulators and investors
Potential for national disclosures and / or guidelines
Disclosures
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Overview of Expected credit losses in IFRS 9
3
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IFRS 9 Expected credit loss model
Scope
Financial assets at amortised cost Financial assets (debt instruments) at FVOCILoan commitmentsFinancial guarantee contractsLease receivables and trade receivables or contract assets Modified financial assetsOverviewIFRS Expected loss model not same as Regulatory EL model (i.e. not TTC).Responsive to changes in information that impact credit expectations.It is inappropriate to recognise full lifetime expected credit losses on initial recognition of financial instruments, except for the simplified approach for trade and lease receivables.Significant increase in credit risk leads to recognition of lifetime losses.
IFRS 9 EL model is data intensive.
Convergence between US GAAP and IFRS has not been achieved.
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Expected credit losses
General model
Effective interest on gross carrying amount12 month expected credit lossesRecognition of expected credit lossesInterest revenueChange in credit quality since initial recognitionStage 1Stage 2
Stage 3
Performing
(Initial recognition*)
Underperforming
(Assets with significant
increase in credit risk since initial recognition*)
Non-performing
(Credit impaired assets)
Effective interest on gross carrying amount
Lifetime expected
credit
losses
Effective interest on amortised cost carrying amount
(
i.e. net of credit allowance)
Lifetime
expected
credit losses
*Except for purchased or originated credit impaired assets
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Expected credit losses
General model
12-month expected credit lossesAre a portion of the lifetime expected credit losses and represent the amount of expected credit losses that result from default events that are possible within
12 months after the reporting date.
Lifetime expected credit losses
The
expected credit losses
that result from all possible default
events over the life of the financial instrument.
Credit loss
The difference between all principal
and interest cash flows that are due to an entity in accordance with the contract and all the cash flows the entity expects to receive discounted
at
the original EIR.
Expected credit losses
The
weighted average of credit losses.
Definitions
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Expected credit losses
General model
Expected credit lossesFinancial assetsECL represent a probability-weighted estimate of the difference over the remaining life of the financial instrument, between:Undrawn loan commitmentsECL represent a probability-weighted estimate of the difference over the remaining life of the financial instrument, between:Present value of cash flows according to contract Present value of cash flows the entity expects to receivePresent value of cash flows if holder draws down
Present value of cash flows the entity expects to receive if drawn down
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Expected credit losses
General model
Assessment of a significant increase in credit risk
Absolute probabilities are not sufficient
Variation between reporting date and initial recognition
Probability of Default
(‘PD’)
12 months unless lifetime assessment is necessary
Counterparty assessment
Maximum
credit risk for a portfolio
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Expected credit losses
General model
Expected credit lossesAn entity’s estimate of expected credit losses must reflect: the best available information.an unbiased and probability-weighted estimate of cash flows associated with a range of possible outcomes (including at least the possibility that a credit loss occurs and the possibility that no credit loss occurs).the time value of money. Various approaches can be used.An entity should apply a default definition that is consistent with internal credit risk management purposes and take into account qualitative indicators of default when appropriate.However…90 days past due rebuttable presumptionSlide 16
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Changes in operating results
Expected
credit lossesGeneral modelChanges in external market indicatorsChanges in credit ratingsChanges in internal price indicatorsChanges in business
Other qualitative inputs
30 days past due rebuttable presumption
However
….
Information to take into account for assessment of increased credit risk
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Expected credit losses
General model
Regulatory PD vs IFRS 9 PDRegulatory PDIFRS 9 PDThrough the cycle(‘TTC’)Point in time(‘PiT’)
Hard to reconcile both!
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Expected credit losses
General model
Discount rate and operational simplificationsDiscount rate for calculating the expected credit lossesEffective interest rate or an approximation thereof.Operational simplificationsLow credit risk: the loss allowance for financial instruments that are deemed low credit risk at the reporting date would continue to be recognised at 12-month ECL.Simplified approach for lease and trade receivablesFor trade receivables or contract assets that do not contain a significant financing component: Relief from calculating 12-month ECL and to assess when a significant increase in credit risk occurred. Lifetime ECL throughout the trade receivable’s life.For lease receivables and trade receivables or contract assets that contain a significant financing component: Accounting policy choice to apply simplified approach to measure loss allowance at lifetime ECL on initial recognition.Slide 1922 October 2014IFRS 9 Implementation ChallengesSlide20
Expected credit losses
Disclosures
QuantitativeQualitative
Reconciliation of opening to closing amounts of loss
allowance showing
key drivers of change
Write
off, recovers and
modifications
Reconciliation of opening to closing amounts of gross carrying amounts showing key drivers of change
Gross carrying amounts per credit risk grade
Inputs, assumptions
and estimation
techniques for estimating ECL
Write
off policies, modification policies and
collateral
Inputs, assumptions and estimation techniques to determine significant increases in credit risk and default
Inputs, assumptions and techniques to determine credit impaired
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Implementation Challenges
4
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Impairment: Implementation challenges
Components
Implementation challenges
Portfolio segmentation
Determine segmentation
criteria.
Consider existing
models and data availability for various portfolios
Criteria for low credit risk
Transfer criteria
Definition of trigger events
Significant deterioration
in
credit
Maturity
Contractual
term
Vs
behavioral
Consideration of prepayments and others
Expected
loss
modeling
Determination
of
models for 12 month and lifetime expected loss
Discount
rate
Forward looking data
Economic
overlay
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Impairment: Key
considerations
Technical analysis and interpretationModelling assumptions/inputs, validation and outputsDisclosuresGovernanceControls considerations
Lack of comparability / benchmarks
Views of regulators
Others
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Impairment : Models to be developed
Portfolio
coverage (by model)Expected loss – 12 months EL, lifetime ELSignificant deterioration of credit
Important questions
Has the entity appropriately segmented its portfolios?
How
is it determined that
the
various models are appropriate?
How strong is the model governance framework?
Is there a consistent basis for model development, validation and documentation?
Is there an appropriate benchmark?
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Impairment :
Level of modelling
Basic approach (?)A simplified approach to ECL by using management judgment to determine provision rates
Specific issues
How to evaluate that management judgment is accurate and correlated to historical data
Is it acceptable under the standards and with the regulators ?
Intermediate approach (?)
Model PD using simple statistical averages.
LGD assumptions are flat
Loss curves are generated using external benchmarks
Economic forecasts included as a management overlay
Specific issues
Substantiate
economic overlays
Insufficient
details in d
evelopment of PD
Advanced
approach
Robust models to incorporate forecasts of macroeconomic conditions used to adjust loss curves.
Loss curves exist for PD, LGD and EAD and are updated both by internal and external data
Specific issues
Challenging
to explain to senior management and investors
Consistence roll out of economic scenarios
Significant overheads
Basic
Intermediate
Advanced
2
3
1
1
2
3
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Impairment :
Leveraging existing credit infrastructure
Banks will consider leveraging existing infrastructureImproves efficiency and minimise rework Align with regulatory model Leverage internal control frameworkTransfer criteriaSignificant deterioration
Economic overlays
Consider economic forecasts based on past events, current conditions and reasonable forecasts of future events
Term structures
Development of lifetime EL, term structure for PD, LGD and correlation
Specific issues and audit concerns
What is considered as significant credit deterioration ?
How can you demonstrate
consistency?
What are the controls over application of significant deterioration?
How to model life time PD and LGD leveraging on existing regulatory and credit models?
How
to
perform back testing with limited availability of data ?
How to determine what economic overlays to be applied ?
How do you judge and evidence the “right economic conditions” and forecasts of the future?
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Impairment -
Leveraging existing Basel methodologies
IFRS 9Basel IIIPD estimated over 12-month horizon for Stage 1; Lifetime loss calculation for Stages 2 and 3
PD estimates are ‘point-in-time’ measures
Definition of default - may adopt regulatory definitions
Considers forward looking estimates at balance sheet date
12-month PD estimation
PD estimates is mostly based on ‘through-the-cycle’ measures
Regulatory overrides
Routine use of stress testing and scenario
analysis to calibrate
IFRS 9
Basel III
Current LGD
Discount rate should be at effective interest rate
Collateral valuation and disclosures for financial instruments with inherent objective evidence of impairment.
Downturn LGD estimates
Consideration of certain costs and LGD floors
Discount rate based upon weighted average
cost of capital or risk-free rate
Treatment of collateral is subject to detailed rules, haircuts etc
Loss Given
Default
('LGD')
Probability of
Default
('PD')
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Impairment –
Data requirements
Key considerationsHow has firm developed processes to collate data from the other systems?Has finance engaged with other business unit to understand the data impact?
Has
the
firm determined the level of automation required to produce the required disclosures in the financial statements ?
Has
the firm
considered the controls over
systems typically outside the statutory audit ?
How to develop process to maintain and update the newly required qualitative/assumption disclosures ?
How comfortable is the firm with the completeness and accuracy of loan level data?
Identify the new data
requirements
Which systems will the data come from - existing
finance reporting
systems and others
?
Data sourcing from
different systems may not be subject to same level of controls and
governance
Identification of appropriate data from right systems
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Business
model
Business models reflect the impact of the IFRS 9ECL models feedback into other strategic processes (e.g. capital management, pricing, stress testing, etc).SystemsAlignment of risk and finance systems?Remapping of lines and accounts within the general and sub ledgersCommon chart of accounts and data definitions across all parts of the business. Data quality
Single data source at required granularity, with full drill down capability and validation of data
Frequent
testing and maintenance of n
ew data models
Automation of data controls
Process
Fully defined processes for identifying the provisions
and how they relate to the business units,
product pricing and strategy.
New credit risk monitoring processes to incorporate system solution to the generation of
accounting information.
Controls and Governance
Circulation
of management reports in a timely manner
Governance and controls over areas not currently subject to statutory
audit
(e.g. Risk and
regulatory data)
Impairment -
Control and governance considerations
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Conclusions
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Key challenges for IFRS 9 implementation
Quality of implementation
Systems and data landscapeResources and timingMateriality OverallStrategic decisionsAffected functions
Full transparency of external and internal factors to be able to make the right decisions
C
hallenges
Mitigation
IFRS 9 impacts the whole group: Group Finance,
Risk
, GTO, r
egional finance, legal entities, business units (CB&S
, GTB, PBC,
AWM, NCOU), senior management
Early inclusion of all potentially affected functions
Clear responsibilities, communication and understanding of impacts
IAS 39
burdens
IFRS 9 phrases certain requirements more clearly than IAS 39 (e.g. modifications)
IFRS 9 implementation could be used to solve issues existing under IAS 39
Identification of requirements and chances to improve accounting
Solving overlaps with other requirements (e.g. forbearance, post AQR topics)
Manage “scope creep”
Interactions
with
other projects
Technical overlaps (e.g. with
FinRep
, BCBS239, CRD IV,
IT
projects)
Potential resource conflicts
Unaligned
project time lines
Identification of all technical and content overlaps
Integrated project set
up
Early decisions on interdependencies and leverage
Capital impacts
IFRS 9 impacts the accounting and regulatory capital
Simulations and strategic policy and business choices
Project set up
Project governance
Budgeting & timing (target application date)
Communication and presentation of strategy
Data
Availability and collection of data
Data definitions
Control and assurance environment
Early data gap and quality analysis
Ability to leverage existing data and processes
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Key lessons learned from on-going engagements with our clients
Lessons learned from the implementation projects completed to date:
Simulation of the quantitative impacts is complex but necessary. The data required to run a fully compliant IFRS 9 EL model is considerable. PwC have experience of running our diagnostic Simulation Tool in over 35 banks of different environmental complexity with varying levels of available data.The transfer between buckets is highly judgmental. Banks need to develop practical policies and guidelines to inform these judgements. Identification of data gaps is critical. The EL model is data intensive. Early effort is needed to identify data gaps and then consider practical solutions to collect and control the necessary data; IFRS 9 impacts are pervasive. IFRS 9 impacts on lending, underwriting and pricing, accounting and reporting, capital and return on equity. Potential to release synergies and efficiencies. It may be possible to leverage existing credit risk methodologies and processes to comply with IFRS 9 requirements without incurring undue cost or effort. Implementation needs to be controlled. PwC
has in-depth IFRS 9 project
management experience and skills, including role allocation and issue resolution experience. We can help you ensure implementation is controlled and achieved in an orderly and efficient manner.
IFRS 9 is of strategic importance.
The strategic impacts of IFRS 9 can be considerable and therefore it is important to understand the impact on the banks business and plan potential responses.
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Questions?
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Thank you for your attentionJohn KellySenior Manager, Banking & Capital MarketsT: +353 (1) 792 8903M: +353 (87) 244 0162john.j.kelly@ie.pwc.com