ifrs 9 implementation challenges 22 october 2014

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

Slide 222 October 2014


Background to IFRS 9: The project and timetable for implementation

1Slide 3

22 October 2014


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.



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.



Classification and measurement

2Slide 6

22 October 2014


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



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.



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



Overview of Expected credit losses in IFRS 9

3Slide 10

22 October 2014


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.



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)


*Except for purchased or originated credit impaired assets




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.


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





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

Slide 14

22 October 2014



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





• 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



Changes in operating results


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




Regulatory PD vs IFRS 9 PD

Regulatory PD

IFRS 9 PD

Through the cycle(‘TTC’)

Point in time(‘PiT’)

Hard to reconcile both!




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.



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



Implementation Challenges

4Slide 21

22 October 2014


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



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



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?



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



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?



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')



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



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



Conclusions



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



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.



Questions?


This publication has been prepared for general guidance on matters of interest only, and does not constitute professional advice. You should not act upon the information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information contained in this publication, and, to the extent permitted by law, PwC does not accept or assume any liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it.

© 2014 PwC. All rights reserved. PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see www.pwc.com/structure for further details.

Thank you for your attention

John Kelly

Senior Manager, Banking & Capital Markets

T: +353 (1) 792 8903M: +353 (87) 244 0162

[email protected]

ifrs 9 implementation challenges 22 october 2014

Documents

new ifrs

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aspects of ifrs

credit requirements

credit losses https

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best available information

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