IFRS 9 Implementation Challenges
www.pwc.ie/banking
22 October 2014
IFRS 9 Implementation ChallengesPwC
Agenda
1. Background to IFRS 9: The project and timetable for implementation
2. Classification and measurement
3. Overview of Expected credit losses in IFRS 9
4. Implementation Challenges
5. Conclusions
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Background to IFRS 9: The project and timetable for implementation
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Effective date and transitionOverview
• 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.
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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 implementation
EU / EFRAG Emerging PracticeIASB
• IASB published IFRS 9 on 24 July 2014
• IFRS 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 FVO
• Early 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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Classification and measurement
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Amortised cost FV-PLFV-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; and
The 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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Key challenges for IFRS 9 implementationC
&M
Con
sid
era
tion
s
•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)
Challenges 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
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IFRS 9 Expected credit loss model
Scope
• Financial assets at amortised cost
• Financial assets (debt instruments) at FVOCI
• Loan commitments
• Financial guarantee contracts
• Lease receivables and trade receivables or contract assets
• Modified financial assets
Overview
• IFRS 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 lossesGeneral model
Effective interest on gross carrying amount
12 month expected credit losses
Recognition of expected credit losses
Interest revenue
Change in credit quality since initial recognition
Stage 1 Stage 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 lossesGeneral model
12-month expected credit losses
Are 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 lossesGeneral model
Expected credit losses
Financial assets
ECL represent a probability-weighted estimate of the difference over the remaining life of the financial instrument, between:
Undrawn loan commitments
ECL 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 receive
Present 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 lossesGeneral model
Assessment of a significant increase in credit risk
Absolute probabilities
are not sufficientVariation
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 lossesGeneral model
Expected credit losses
• An 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 presumptio
n
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Changes in operating results
Expected credit lossesGeneral model
Changes in external market
indicators
Changes in credit ratings
Changes in internal price
indicators
Changes in business
Other qualitative inputs
30 days past due
rebuttable presumptio
nHowever….
Information to take into account for assessment of increased credit risk
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Expected credit lossesGeneral model
Regulatory PD vs IFRS 9 PD
Regulatory PD
IFRS 9 PD
Through the cycle(‘TTC’)
Point in time(‘PiT’)
Hard to reconcile both!
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Expected credit lossesGeneral model
Discount rate and operational simplifications
Discount rate for calculating the expected credit losses
• Effective interest rate or an approximation thereof.
Operational simplifications
• Low 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 receivables
• For 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.
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Expected credit lossesDisclosures
Quantitative Qualitative
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
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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 interpretation
Modelling assumptions/inputs,
validation and outputs
Disclosures
Governance
Controls 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 EL
Significant 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 development 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 infrastructureBanks will consider leveraging existing infrastructure
- Improves efficiency and minimise rework - Align with regulatory model - Leverage internal control framework
Transfer criteria
• Significant 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 9 Basel III
• PD 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
Giv
en
Defa
ult
('L
GD
')
Pro
bab
ilit
y of
Defa
ult
('P
D')
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Impairment – Data requirements
Key considerations
How 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 9
• ECL models feedback into other strategic processes (e.g. capital management, pricing, stress testing, etc).
Systems • Alignment of risk and finance systems?
• Remapping of lines and accounts within the general and sub ledgers
• Common 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 new 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 andregulatory 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 landscape•Resources and timing•Materiality
Ove
rall
Strategic decisions
Affected functions
•Full transparency of external and internal factors to be able to make the right decisions
Challenges Mitigation
•IFRS 9 impacts the whole group: Group Finance, Risk, GTO, regional 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
Pro
ject
set
up
•P
roje
ct g
ove
rnan
ce•
Bu
dg
eti
ng
& t
imin
g (
targ
et
ap
pli
cati
on
date
)•
Com
mu
nic
ati
on
an
d p
rese
nta
tion
of
stra
teg
y
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 clientsLessons 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 attention
John Kelly
Senior Manager, Banking & Capital Markets
T: +353 (1) 792 8903M: +353 (87) 244 0162