2559 “forward-looking pds for ifrs9 loan impairment” · macro hedge accounting separate project...

การประชมวชาการ

“ศาสตราจารยสงเวยน อนทรวชย ดานตลาดการเงนไทย” ครงท 24 ประจาป 2559

การบรรยายเรอง “Forward-Looking PDs for IFRS9 Loan Impairment”

ผบร รยาย คณณฐวฒ แวงวรรณ

วนศกรท 25 พฤศจกายน 2559 เวลา 15:00 – 16:30 น. หอง 205 คณะพาณชยศาสตรและการบญช มหาวทยาลยธรรมศาสตร ทาพระจนทร

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Forward-Looking PDs for IFRS9 Loan Impairment

Natthawut Wangwan VP, Head of Risk Modeling and MIS Department

Kiatnakin Bank Public Company Limited

The views expressed herein are my own and do not necessarily represent the views of Kiatnakin Bank Public Company Limited

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Agenda � Overview

� Impairment Model

� Implementation

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IFRS9 Overview

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The IASB has issued the final version of IFRS 9 Financial Instruments on 24 July 2014 – Mandatory retrospective application 2018

Classification and Measurement

Impairment

General Hedge Accounting

Macro Hedge Accounting Separate project

25 พฤศจกายน 2559 การประชมวชาการ “ศาสตราจารยสงเวยน อนทรวชย ดานตลาดการเงนไทย” คร !งท" 24

IFRS9 Overview

4

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Financial Assets • New classification model with three

categories • Valuation dependent on contractual

cash flows as well as business model

1. Amortized cost (AMC) 2.Fair Value Through Profit & Loss (FVTPL) 3. Fair Value Through Other Comprehensive Income (FVTOCI) • FVTOCI for certain debt and equity

instrument

Financial Liabilities • FVTPL – Valuation for trading

liabilities • Fair Value Option : Change in own

credit will be permanently recorded in other comprehensive income

ancial Assets

1.Classification & Measurement 2. Impairment 3. Hedge Accounting

• New Expected Loss (EL) – Model, based on 12 months or lifetime EL dependent on the level of credit deterioration

Stage 1 Stage 2 Stage 3

Initial recognition (if not impaired yet)

Significant increase of expected credit loss

Objective indications for impairment (credit impaired)

Expected Loss Calculation p12 –

month EL Full Lifetime EL

Apply effective interest rate to

Gross carrying amount Net

carrying amount

• Stronger connection with risk management

� More accepted types of hedge transactions

� Less P&L volatility • No prospective effectiveness test

within 80-125% thresholds required

• No retrospective effectiveness test • But : no voluntary de-designation

anymore • Initial choice to keep IAS39 hedge

accounting model until macro hedging project finalized

• Additional choice to keep IFRS 9 portfolio fair value hedges as protection against interest rate risks

Medium impact on results, processes and IT Medium High High impact across the target

operating model Low Impact anticipated to be limited

Pha

se

Key

Ch

ange

s Im

pact

การประชมวชาการ “ศาสตราจารยสงเวยน อนทรวชย ดานตลาดการเงนไทย” คร !งท" 24

IFRS9 Key Consideration

25 พฤศจกายน 2559 5

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Q1 2014 Q2 2014 Q3 2014

IAS 39 IFRS 9

IAS 39 IFRS 9

1. Impairment Requirements 2. Financial Impact 3. Implementation complexities

New general impairment model create the biggest challenge

Change in credit risk

Stage 1 Initial

recognition

Stage 2 Significant increase in credit risk

Stage 3 Objective

Evidence of Impairment

Loss Allowance

1 year EL Lifetime EL

Interest revenue

Net basis Gross basis

Accounting Treatment & Disclosure Credit risk management • Assumptions, methodologies, inputs,

techniques and policies

Expected credit loss evaluations • Movements between stages • Reconciliations

Credit risk profile • Increased granularity

Impairment stock anticipated to increase upon transition

Impairment stock

Impairment volatility also to significantly increase post transition

Volatility

Changes require early and ongoing quantitative impact assessment to: • Prepare communication of change to key

stakeholders • Inform key design choices including:

o Model methodology o Stage 2 and 3 cut-offs

Complex implications across multiple dimensions of the operating model.

Models

Data

IT

Controls

Reports

People

• Scoring, pricing • PD, LGD and EAD • Behavioral lifetime

• Data history • Operational data

• Source systems • Datamarts • Calculators

• Governance • Model governance • Process controls

• Internal & external • Quantitative & qualitative

• Risk & Finance roles & responsibilities

• Skills and resources


Impairment Focus Areas


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การเปรยบเทยบหลกเกณฑปจจบนและ IFRS 9

IFRS 9

Classification & Measurement

• Business Model & CF Characteristic (1) FVTPL (2) FVOCI (3) Amortized cost • Interest revenue recognition -

Amortized cost : Effective Interest Rate ((EIR)

Impairment

• Expected loss (EL) - 12-month EL - Lifetime EL • Exposure + Off B/S (Loan commitment) • Discount rate : EIR

Hedge Accounting

• Fair Value Hedge • Cash flow Hedge • Net Investment Hedge

หลกเกณฑปจจบน (ตามมาตรฐานบญชและเกณฑของ ธปท)

ผลกระทบ

• Objective (1) Trading (Fair Value) (2) Available for sales (Fair Value) (3) Held to Maturity (Amortized Cost) (4) General investment (Cost) • Interest revenue recognition - Amortized cost: Contractual rate

• กนเงนสารองตามการจดช �น • ช �นปกตและช �นกลาวถงเปนพเศษ : 1% และ 2% ของยอดสนเช"อหกหลกประกน

• NPL: 100% ของยอดสนเช"อและภาระผกพนนอกงบ CCF 1 หกหลกประกน

• Hedge accounting ตาม IAS 39 • Accrual Hedge: แสดงรายการตราสารอนพนธ

เปนรายการนอกงบดล และรบรรายได/คาใชจายดอกเบ �ยตามเกณฑคงคาง

ตองจดประเภทตราวานทนประเภทเงนลงทนท"วไป (วดดวยราคาทน) ใหมเปน

FVTPL หรอ FVOCI (วดดวย FV)

มผลตางระหวางรายได/คาใชจายตาม EIR

กบจานวนเงนท"รบ/จายจรงตามสญญา

กนเงนสารองเพ"มข �น เน"องจาก • กนเงนสารองแบบ EL • กนเงนสารองสาหรบภาระผพนนอก

งบดลของลกหน �ท"ไมไดเปน NPL

ตองจดเตรยมระบบ IT และฐานขอมลเพ"อคานวน EL และ EIR

หากไมใช Hedge Accounting ตอง MTM

ตราสานอนพนธทกรายการเปนสนทรพย/

หน �สนตราสารอนพนธ

"

ตลต1

มผมผ2

กก3

ตต4

หาห5

การประชมวชาการ “ศาสตราจารยสงเวยน อนทรวชย ดานตลาดการเงนไทย” คร !งท" 24 25 พฤศจกายน 2559 7

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เกณฑการกากบดแลของ ธปท.

หลกเกณฑการจดช นและการกนเงนสารอง

การจดชน การกนเงนสารอง

ปกต

กลาวถงเปนพเศษ

1% เงนใหสนเชอ


สงสญจะสญ สงสญจะสญ

NPL

ตากวามาตรฐาน

สงสย


และรายการภาระผกพน นอกงบดล

แนวทางการกนเงนสารอง

CL และ NPL

หลกเกณฑการรบรรายไดดอกเบ ย

Current Loan (CL) Possible

Impaired Loan (PIL)*

รบรรายไดดอกเบย ตามเกณฑคงคาง

หมายเหต * ลกหนจดชนกลาวถงเปนพเศษ เกณฑเชงปรมาณและเกณฑเชงคณภาพ/ลกหนปรงปรงโครงสรางหนทจดชนปกต/ลกหน Reschedule ทจดชนปกต/ลกหน watch list ทจดชนปกต


หยดรบรรายไดดอกเบย และกลบรายการรายได

ดอกเบยทเคยรบร


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IFRS9 Impairment Model

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Change in credit risk

IFRS9 Impairment : Expected Credit Loss Model


| | การประชมวชาการ “ศาสตราจารยสงเวยน อนทรวชย ดานตลาดการเงนไทย” คร !งท" 24

IFRS9 Impairment : Expected Credit Loss Model


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When assessing whether the credit risk on a financial instrument has increased significantly, an entity shall do the following:

� Look at change in the risk of default occurring over the expected life rather than the change in ECL.

� Compare risk of default at reporting date with risk of default at date of initial recognition

� Consider using a combination of quantitative and qualitative triggers, as below.

Changes in credit ratings • Drop in external

credit ratings • Drop in internal

credit ratings

Changes in internal price indicators of credit risk • Significant

deterioration of loan to value ratio

• Breaches in financial covenants

Changes in external market indicators • Drop in borrower’s

bond prices • Increase in credit

default swap prices for borrower

Policy choice

Low credit risk

Approximation • 12month PD • Assessment on borrower level • Consistent thresholds on portfolio

level

Rebuttable assumption

More than 30

DPD

Stage 1 Stage 2

rket

wer’s

edit prices Changes in

business financial or economic condition • Industry downturn

Changes in operating results • Actual or expected

decline in revenues/margins

• Working capital deficiencies

Other qualitative inputs • Litigation likely to

have material impact

Quantitative triggers

Qualitative triggers


Significant increase in credit risk (Transfer out of Stage 1 to 2)


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Lifetime expected losses are generally expected to be recognized before a financial instrument becomes past due

Potential credit risk indicators

Lagging Leading

Past due Information

Forecast changes in business, financial or economic conditions

Re-price/ Forbearance

Changes in credit risk on other products held by the borrower

Changes in the performance and behavior of the borrower

Changes in prices of borrowers debt and equity instruments


Significant increase in credit risk (Transfer out of Stage 1 to 2)


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Credit impaired

Concession

Bankruptcy

Deep discount

Disappearance

Financial difficulty

Breach of contract

Lenders grant a concession relating to the borrower’s financial difficulty

Probable bankruptcy or other financial reorganization

Purchase or origination of a financial asset at a deep discount that reflects the incurred credit losses

Disappearance of an active market for that financial asset because of financial difficulties

Significant financial difficulty of the borrower

Breach of contract (e.g. past due or default)

Definition of default � When defining default for the

purpose of determining the risk of a default occurring, an entity shall apply a default definition that is consistent with the definition used for internal credit risk management purpose for the relevant financial instrument and consider qualitative indicators (for example, financial covenants) when appropriate.

� Definition of default must be applied consistently to all financial instruments.

� There is a rebuttable presumption that default does not occur later than when a financial asset is 90 days past due.


Objective Evidence of Impairment (Transfer out of Stage 2 to 3)


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ECL = PD% x LGD% x EAD$

�Expected credit loss is a statistical measure used to reflect expectations of future losses based on historical data �The three primary components are derived based on observation, empirical evidence and export judgment

�Probability of default for an asset or class of assets over the next year �PD represents an average expectation over the course of an entire business cycle (through-the-cycle) as opposed to specific current expectations(point-in-time)

�Loss given default based on losses resulting from defaults �Ideally the LGD will be separated for secured and unsecured portions of an exposure �LGD is a prudent parameter based on an assumed downturn in the economic conditions

�Exposure at default represents the amount a financial institution stands to lose in the event of a default event �Undrawn commitments are factored in using statistical probabilities of drawing


Expected Credit Loss Model - Overview


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Choosing ECL Modeling methodology

One of the main criteria in choosing the modeling methodology for EL calculation is the availability of data. For those portfolios with affluent data, quantitative model generally is the preferred methodology. Whereas for portfolios with minimal data or low default/ loss data, Expert Judgment Models is the preferred methodology.

Quantitative Models

• Quantitative models are statistical models developed based on affluent data.

• The accuracy of the models are heavily reliant on the quality of internal data (completeness, coverage, timeliness, etc.) and bank’s internal practice.

Hybrid Models

• The development of the hybrid models is usually based on a mix of internal and external data.

• Such models are a hybrid combination of empirically built statistical and expert judgment model.

Expert Judgment Models

• Characteristics of this model are the utilization of constrained expert judgment, and/or benchmark such as industry data, Basel requirement benchmark, etc.

• This is a potential solution for Banks either without reliable historical data or insufficient data.

Historical Data Availability

Judgment and Benchmarking


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Risk Ratings & Regulatory Evolution

Basel I Basel II (TTC)

IFRS 9 (PIT)

Stress Testing (PIT)

Regulatory Evolution

Single Internal Rating

Internal AIRB Models Determine TTC Rating

Requirement from EBA: PIT should be

forward looking projections of default rate and loss rates and capture current

trends in the economy cycle

PD used for IFRS9 should be PIT probabilities

(probability of default in current

economic conditions) and do contain adjustment

for prudence

90S 2002-07 2014 2015-2020


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Basel and IFRS Expected Credit Loss Differences In Calculation

�Borrower quality �Term �Covenants

ECL PD LGD EAD �Facility related �Seniority �Cover type & Cover value

�Facility related �Degree of commitment �Repayment schedule

�Through-the cycle(TTC) �Model driven (long run historical average)

�Single-period estimation(12-month time horizon)

�Subject to qualitative requirements

�Downturn LGD �Economic concept �Includes cost of capital and allocation of overheads

�Based on historical values subject to qualitative requirements

�Downturn EAD �Use of long observation period (5 or 7 years)

�Off-balance sheet items are converted with a downturn CCF

�Point-in-time (PIT) �Market implied �Multi-period estimation (stage 1:12 month, stage 2&3 lifetime)

�Based on market-values and recovery assumptions

�Regulatory LGD can be applied

�Recovery concept �Does not include future expense of collection

�Internal loss estimates for impaired assets only

�Time-dependent EAD �A specific period of time with respect to the security

�Inclusion of off-balance sheet items is under discussion

Basel AIRB

Used to calculate expected credit losses (ECL) and unexpected credit losses (UCL)

IFRS 9

Calculated as the difference between carrying value and the present value of the estimated future cash flows

= x x


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Basel and IFRS Expected Credit Loss


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Life-time PD curve can be derived from data showing historical observed default rates split by vintage year. The steps to derive this generic curve are described below.

Step 1: Use annual delinquency tables of default rates by vintage and year of development from origination to determine the vintage default experience by year from origination of a particular loan.

Step 2: Use the adopted average experience based on the marginal PD to fit a curve. This gives us the marginal PD curve for a particular segment.

Step 3: The curves produced per segment by step 2 are then compared. Segments with similar profiles can be combined to reduce complexity. If profiles differ significantly between segment, curves from step2 can be used directly for their relevant segments.

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

0 1 2 3 4 5 6

Cum

mul

ativ

e de

faul

t rat

e

Year from origination

Cumulative default rate by year of origination

2008 2009 2010

Adopted Average 2011 2012

2013 2014

0.00%

0.30%

0.60%

0.90%

1.20%

1.50%

1.80%

0 1 2 3 4 5 6 7

Marginal default rate by year from origination

Adopted average Fitted curve to adopted average

*Life-time PDs can be derived from models which Banks already have in place; such as pricing models or AIRB models


Lifetime PD Curve - Retail


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Cohort analysis or migration analysis, Bayesian approach whilst internal data is collected. Lifetime PD curve differs to shape between the grades.

0.00%

0.40%

0.80%

1.20%

1.60%

2.00%

1 2 3 4 5

PD

Year

Rating BAA

PD - cohort analysis PD - migration analysis

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

1 2 3 4 5

PD

Year

Rating B

PD - cohort analysis PD - migration analysis

Data source: Moody ‘s Credit Policy March 2008, Corporate One-to-Five-Year Rating Transition Rates


Lifetime PD Curve – Non-Retail likely to leverage external data


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� The Markov property is that the probability of an object occupying any given future state depends only on its current state.

� A transition matrix describes a set of transition probabilities that fulfill this requirement.

If V(0) is vector of current states then:

V(t)=V(0).Mt

� In a more general form M may change over time M(t)

To

From


Transition Matrix : Markov Chain


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� In a more general form M may change over time M(t). � It is useful to be able to describe a single parameter shift that stresses a matrix to

plausible forms of M(t). Z-shift is one popular approach

Cumulative Inverse Normal

Shift (eg add 0.1)


Transition Matrix : Markov Chain Z-Shift approach


| | การประชมวชาการ “ศาสตราจารยสงเวยน อนทรวชย ดานตลาดการเงนไทย” คร !งท" 24

Simple Linear Regression: historical path and level will be projected into the future as the forecast.

Econometric Model

• y is the default rate • X is the matrix of macro economic factors • Z is the matrix of controlling factors for credit quality and age. Average score at initiation and average month-on-book can be used as the proxy

� The data requirement is more demanding if we would like to capture the impact of maturation, credit quality and exogenous factors all together.

� To capture the impact of each of these dimension, we may use either dummy variables (which represent each of the dimension) or proxy variables.

� Adding one more dimension will consume one more degree of freedom.


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IFRS9 Implementation

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Implementation Challenges High-level Impact of IFRS 9 to Financial Institution

Accounting

Policies

Markets /

Front Office

Processes

Governance

IT System

Finance, Risk

and IT Staff

Budgets

� New guidelines for Classification &

Measurement, Impairment, Hedge Accounting

� Potentially, amended guidelines for

Management Accounting – depending on the

decision how far IFRS 9 should be reflected

� Consideration of Business Model impacts as well

as Cash Flow Characteristics on IFRS 9

classification, adjusted NPP*

� Consideration of IFRS 9 impacts on pricing, e.g.

for loans

� Links Risk and Finance processes especially

regarding impairment

� Adjusted processes for Classification and Hedge

Accounting

� Adjusted NPP

� Clarification of future IFRS 9 responsibilities of

Finance, Risk and Controlling necessary

� Set-up of IFRS 9 project governance

� IFRS 9 will have impacts on all IT layers, e.g.

new attributes, new evaluation functions,

adjusted booking rules, adjusted reporting

� Parallel processing for comparison year(s) is

challenging

� IFRS 9 will require significant internal support

during project time

� Releasing staff from regular tasks through

external support should be considered

� Know How on ongoing basis necessary

� IFRS 9 will at least have impacts on 2018 and

2019 budgets

� Gap analysis for enabling reliable budget

estimation should be started soon


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IFRS9

Products and volume

Revenue Growth Pricing

Operating Margin

Capital Liquidity

Reputation

Portfolio and product mix

Market position relation to peers

Disclosures and market discipline

External audit

External rating, reflecting increased P&L volatility

Cost of funding

Stakeholder expectations

Pillar 2B capital planning buffer for drawn down in a stress

Pillar 1 and 2A capital requirements

Balance sheet impact of IFRS 9 provisioning, including step change upon introduction

Basel 3 Tier 1 and Tier 2 capital instruments and leverage ratio

P&L impact of IFRS 9 provisioning, including on-going volatility

Risk and Finance operating model efficiency • People • Processes • Data & systems • Policies • Models • MI & Reporting

Risk adjusted pricing

ExE

P

s


Implementation Complexities


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Impacts on credit risk frameworks

Every bank approaches the management of credit risk slightly differently; set out below is a generalized credit risk management framework. If a credit risk management process is used for IFRS 9 purposes, it will become within the scope of the audit.

Product design/review

Origination

Risk measurement

Risk monitoring and reporting

Risk transfer/exit

Credit risk management framework

Product design/review

� The process in which new products are designed

Origination � The process by which potential obligors are assessed and new loans are recognized on the book

Risk measurement

� The process where risk within the portfolio is qualified

Risk monitoring and reporting

� The reporting and analysis of risk within a portfolio.

� The process where bad loans are worked out

� Where impairment is raised

Risk transfer/exit


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Implementation Process A three-step implementation process will involve: 1. Sourcing of reconciled data through the bank’s appropriate systems of record. 2. Running the loan-level data along with other finance & risk characteristics through the IFRS 9 engine to generate the relevant

balances and provision 3. Generating a comprehensive reporting solution displaying movements in amounts and relevant statutory disclosures

Data Warehouse and ETL IFRS 9 Calculation Engine Reporting and disclosure Engine Data Warehouse and ETL IFRS 9 Calculation Engine Reporting and disclosure Engine

Origination Records

Loan transaction

GL Data sources

Loan Accounting

Scoring System

IAP Information

Phase 1 Classification Logic

Phase 2 Impairment Calculation Engine E

PD Term Structure

neStress Testing

Linkages

LGD Tables MEV forecast

EAD Rules Effective interest rate interest rate

12 Month & Lifetime Expected Loss

Staging and Impact Assessment

Phase 3 Hedge Accounting Logic

Risk

Dat

a w

areh

ouse

Regulatory Reporting Systems

Management Reporting Systems

Accounting System

Financial Disclosures

• Reclassification of Assets and Liabilities

• Movements between 12mth and LT ECL

• Transfer between collective vs specified provision

• Transfer between not credit impaired vs credit impaired


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Key findings from Deloitte’s Sixth Global IFRS Banking Survey in 2016

Total estimated programme Budgets continue to increase. However, more than three quarters of these Budgets have yet to be spent, with less than 2 years to transition date

60% of banks either did not or could not quantify the transition impact of IFRS 9. Of the banks who responded, the majority estimate that total impairment provisions will increase by up to 25% across assets classes

Most price makers expect that moving to an ECL model will have an impact on product pricing, while most price takers still think that this is unlikely to have an impact on product pricing

Data quality and, in particular, the availability of the origination Lifetime PD, is the biggest data concern for the majority of banks

Almost half of banks think they do not have enough technical resources to deliver their IFRS 9 project and almost a quarter of these do not think that there will be sufficient skills available in the market to cover shortfalls.

70% of respondents anticipate a reduction of up to 50 bps in core tier 1 capital ratio due to IFRS 9. The vast majority does not know yet how their regulators will incorporate IFRS 9 numbers into regulatory capital estimates.

In general, approximately half of participants are unsure of the answer to many key modeling design questions, which may delay hank’s IFRS 9 programmes.

Despite IAS 8 requirements and EDTF recommendations, over40% of banks do not plan to disclose quantitative information before 2018


Industry Perspective


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Global IFRS Banking Survey


Assuming today’s credit environment were to apply, how is your bank’s total impairment provision likely to change on transition to IFRS 9?


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Reference


Document from

� Bank of Thailand (www.bot.or.th)

� Deloitte (www.deloitte.com)

� EY (www.ey.com)

� IFRS (www.ifrs.org)

� KPMG (https://home.kpmg.com)

� PwC (www.pwc.com)

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Thank you

www.kiatnakin.co.th

2559 “forward-looking pds for ifrs9 loan impairment” · macro hedge accounting separate project...

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