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Moody’s Analytics

IFRS 9 for Insurers

Roshni Patel and Nadja Roos Wednesday, 26 September 2018

IFRS9 for Insurers, London, September 26, 2018 2

Moody's Analytics operates independently of the credit ratings activities of Moody's Investors Service. We do not comment on credit ratings or potential rating changes,

and no opinion or analysis you hear during this presentation can be assumed to reflect those of the ratings agency.


AgendaKey elements for discussion

1. Current trends in the insurance market

2. Best practices in gearing up for implementation

3. Acceptable model methodologies and level of granularity

4. A solution considering the challenges and linkages to IFRS 17


Accounting Standard Timelines

2022

CECL - SEC Filers

IFRS17 adoption*

IFRS9 exemption for Insurers

CECL - Other Co.’s

202120202019

IFRS9 General

adoption

2018

CECL - Public Co.’s

* IFRS 17 general adoption. Early adoption possible. Some regulators might decide to accelerate timelines.


Stages to IFRS9 complianceSmall steps achieving greater affect


Implementation Timeline – an Insurance view

IFRS 9: Automation and

Workflow

Integration with

IFRS17/Solvency II

IFRS 9 compliance –

tactical approach

Methodology development

/ enhancement

IFRS 9 compliance –

tactical approach

Q3 2018 Q1 2019 Q3 2019

Q4 2018 Q2 2019 Q4 2019

Gap assessment / Impact Study

Diagnostic Phase

Business Model and SPPI tests


Current market themes

Results of Gap Analysis / Impact Study

» What were some of the key findings?

» What existing methodologies, tools are recommended for use in IFRS9 implementation?

» If Impact study, what are the initial provision estimations, communication around the analysis?

Path to Implementation

» With late Q1 2019 / early Q2 2019 start, when is the parallel run targeted to begin?

» What is the planned scope of the parallel run?

» Is the firm planning to engage external support for implementation assistance, model validation, accounting advisory?

Applicable portfolio and materiality compared to IFRS17?

» Investment vs applicable portfolio

» Others standard, implementation unclear, i.e. UK GAAP?

1 Advantages of early

Implementation


Advantages of early ImplemetationBest Practices – Incorporating lessons learned

1Accountability / Responsibility

Institutions identifying a ‘Super user’ early on in the implementation project saw

considerable synergies.

2Governance

Strong Project Governance with Senior Management involvement and clear

escalation procedures made projects more efficient from the start.

3Validation / Audit

Early engagement of internal validation and external audit teams brought

everyone on the same level simultaneously and focused the formal process.

4Global / Local

Local Workshops with detailed user training across the end to end calculation

process.


Advantages of early ImplemetationBest Practices – Modelling

1Scenarios

Explore the use of internal macroeconomic scenarios for consistency with

Stress Test / ICAAP.

2PD / LGD

Use internal LGD models for consistency if available. Incorporate conditional PiT

PD / LGD term structures into existing accounting systems.

3Consistency

Consistency with internal process (Credit process, watch list approach, ICAAP,

Stress Testing).

4Validation

Incorporating new models in existing operating model (e.g. expanding current

Validation Framework).


Advantages of early ImplemetationInternal communication and Incorporating results

1Parallel Run

Opportunity for a longer parallel run phase.

2IFRS9 Impact

Analysing the impact of IFRS9 and determining potential mitigants (credit review of

stage 3, determining impact of staging in stress scenarios, QoQ volatility).

3Internal Communication

Providing Senior Management (CEO, CRO, CFO, CTO) with frequent and tailored

information especially around the QoQ volatility.

4Infrastructure

Assessing possible solutions in the context of existing infrastructure and potential

technical POCs. Enhance data systems and resources to source and link all data

requirements.


Advantages of early Implemetation

» Type of resources is changing

» Bringing skills into BAU

» Understanding of Risk and Finance data and

processes

» New sub teams for reporting across

stakeholders (CFO, CRO, CTO)

Resource Mix

Global Resources and Local Resources

» Addressing and assessing Group requirements

vs. Subsidiary requirements

» Implementing / communicating local changes

Consistency

» Have resources / skill sets that look across a

number of different requirememts (ST, ICAAP,

IFRS9)

People Cost

» Data – enhancing Risk and Finance data

» Personnel cost

» Methodology changes, re-calibration annually and

assumptions reflections

Operational Cost

Infrastructure

» New IT infrastructure vs. building/ re-using current

IT Infrastructure

» Tactical vs. Automated solution

Business Cost » Adopting new regulation / modifying existing processes

» Increased internal validation efforts

» Ongoing maintanance of models / documentation

» Enhancing business requirements (e.g. Origination,

RAROC)

Considering Operational Aspects of IFRS9

2 Portfolio Coverage


Insurance Market: Asset distribution

* The above sample was constructed by considering average asset distribution from US Life insurance firms and European insurance. In case of European insurance, not all structured securities are

reported.


Business Model and Classification & Measurement

• Perform Business model (BM) Assessment

• Test Criteria for Solely Payment of Principal and Interest (SPPI)

• Coverage: Products and investments and discuss the future balance sheet structure with key stakeholders

• Identify new policies and procedures e.g. business model policy that needs to be developed

BM and SPPI Test

• Assessment of systems, policies and procedures for BMA and SPPI test

• Content and structure of business concept; definition of benchmark test including business requirement description

Accounting Processes Revision

• Advise on implementation of models, data requirements, policies & procedures for BM and SPPI Test

• Automation to perform BM and SPPI test

Implementation

3 Impairment Modelling

Requirements


Impairment Process Experience – IFRS9Typical Process Steps

Data

Management

Portfolio

Segmentation

Methodology/

Models Selection

Scenario

Design/

Selection

Model(s) &

Accounting

Execution

Qualitative

Adjustments

Framework

Data preparation, Data Load,

ETL/Manual, Exception

reporting, Data substitution,

Rules Engine

CECL/IFRS9-allowable

Methodologies and Models

feeding these methodologies,

Assumptions

Model and Accounting Engines

execution

Ad hoc and/or pre-approved

segmentation at any level of

granularity

One or multiple Scenarios and

their weights, Reversion (if

desired)

Management Override at any

level, interagency and CECL

qualitative factors, entity-

specific factors

Real time reporting on input and output data, trending, roll forwards, audit trails, preparer/approver for any changes, attribution, disclosures, GL

aggregate and instrument-level entries

Pu

blis

h a

nd

Extra

ct (D

ata

an

d G

L)


IFRS 9 Impairment Calculation StepsAn end-to-end process

1Macroeconomic Scenarios

• Minimum of 3 Scenarios – Benign, Baseline and Adverse

• Include factors that are credit explanatory

• Quantitative Weights for each scenario (e.g., 50% weight for Baseline)

• Forecast Horizon: Cover full portfolio maturity (typically > 30 years)

3Stage Allocation

• Separate Allocation criteria per portfolio (Stage1 – 12m ECL, Stage 2/3 – L/time ECL)

• Analyse portfolio migration across stages to choose most optimal criteria per segment

• Absolute and Relative Thresholds

• Comply with minimum standards and best practice

4ECL calculation

ECL Calculation and Calibration

• Define and calibrate Effective Interest (Profit) Rate and discount factors

• Forward exposure method for ECL calculation

• Review and qualitative adjustments

2

PD & LGD Modelling

Forward-looking PIT PD Term Structure

• Derive TTC PDs from historical ratings, mapping to Moody's Analytics default study

• Convert TTC PDs to PIT

• Induce Scenario dependence to convert PIT PDs to Forward Looking PIT PDs

Forward-looking PIT LGD Term Structure

• TTC LGD based on historical recoveries, collateral and/or off-the-shelf models

• Lifetime LGD and Moody's Analytics PD – LGD Correlation model


Impairment Model Overview

Macroeconomic Scenario Forecasts

Scenario Probability Weights

Macro-Conditioned, Point-in-Time, Forward-Looking Default Probabilities

Credit Stage

PIT Conversion

Default Risk Measure

» Forecasts of GDP, unemployment, prices, etc.» Minimum 3 alternative scenarios: baseline, upside, downside» Business relevant scenarios

» Expected probabilities of scenarios

» Stage 1, 2, or 3 based on credit risk

» If TTC, need to convert to PIT

» PD Model» External or internal rating

Expected Credit Loss

Loss Given

Default

Exposure at

Default

Discount Factor

XX =


Forward Looking & Probability-Weighted Outcomes

» Requires expected credit losses (ECL) to account for forward-looking information

» Requires probability-weighted outcomes when measuring expected credit losses

– Estimates should reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs

Macroeconomic modelling satisfies both requirements above

IFRS 9


Macroeconomic ScenariosProbability-weighted expected loss

2007Q1 2010Q4 2014Q3 2018Q2 2022Q1 2025Q4

2012Q1 2014Q1 2016Q1 2018Q1 2020Q1

S1 S3Baseline

Multiple Scenario Forecasts

Derive probability-weighted ECL

Scenario probabilities

S2 Mild Second Recession

Protracted SlumpS4

Baseline / Most LikelyBL

New Upside ScenarioS0

Stronger Near-Term ReboundS1

S3 Deeper Second Recession


United Kingdom Unemployment rate, %

United Kingdom Inflation, % change yr ago United Kingdom House price, 2010=100

United Kingdom IFRS9 Scenario Forecasting

-1.00

0.00

1.00

2.00

3.00

4.00

5.00

S4 S3

S1 S0

BL

2

3

4

5

6

7

8

9

S4 S3 S1

S0 BL

90

110

130

150

170

190

S4 S3

S1 S0

BL

United Kingdom Real GDP, 2015 bil GBP

1600.00

1700.00

1800.00

1900.00

2000.00

2100.00

2200.00

2300.00

2400.00

2500.00

S4 S3

S1 S0

BL

Sources: ONS, Moody's Analytics Analytics


Expected Credit Loss CalculationCalculations steps

Moody's Analytics Analytics IFRS 9 Proposal

TTC ratings

• TTC assessment of creditworthiness

• Rating based on financial information of the obligor

TTC PD calibration

• Calibrate TTC ratings to TTC PDs for each obligor

• Calibration needs to use the historical default experience of the bank

• For portfolios with low number of defaults, Moody's Analytics can augment the data with its own datasets

Translation to PiT PDs

• Adjust TTC PDs for the point in the credit cycle

• Typically banks do not have enough data to extract the credit cycle

• Moody's Analytics can use its own data to get a robust estimate of the credit cycle

Forward-looking PiT PDs

• Add dependence of PiT PDs to macroeconomic variables

• Combine with macroeconomic scenario forecasts and probabilities to get forward-looking estimates


What is the Rating to PD Converter?

PD by

Rating

Country

adjustment

Sector

adjustment

Point-in-Time

PDs

» Use the public firm PD database to

estimate the typical PD given the rating

» Adjust for sector and country trends

» Use the PD term structure to generate a

Point-in-Time PD term structure

» Can be applied to a financial institution’s

internal rating


Addressing the data challenges: LGDsLeveraging PD-LGD correlation when internal data is insufficient

IFRS 9 and beyond: Implementation challenges and what lies ahead

» Use internal recovery data with EIR discounting

– Low default count and poor historization can make this approach unfeasible

» Use PD-LGD correlation

– Empirically and theoretically supported

– Historical pattern of PDs to drive LGD movements

– Linking PD to macroeconomic variables implicitly leads to

macroeconomic dependence of the LGD

– Fine-tune correlation parameter so that historical loss rates match

PD*LGD levels

0%

1%

2%

3%

4%

5%

6%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015

DRLGD

PD-LGD correlation

Default Rate LGD Loans LGD Bonds

PD-LGD correlation = 50%

PD-LGD correlation = 77%


Stage Allocation Criteria

Absolute Criteria

PD Master Scale

Future Default Rate

under each scenario

Portfolio Distribution

Possibility to segment by model,

rating scale or industry

Define

Absolute

Rating

Threshold

Relative to

Inputs

Possibility to

differentiate

by Maturity /

Lifetime PD

Horizon

Absolute Criteria

Rating is less than XX AND / OR PD is > XX%

Last allocated stage

Terms Modified With Adverse Impact on the

bank

Watch list account

Relative Criteria

Decrease in Ratings by XX notches AND / OR Increase in PD by XX

percentage points

XX number of Movements across Stages in the last

XX months

Decrease in collateral coverage ratio

4 IFRS17


Connecting IFRS 9 and IFRS 17Consistent credit modelling / Infrastructure

IFRS 9

Modelling impairments

1. Insurers are looking for consistent approach to credit modelling across both sides of the balance sheet i.e. IFRS 9 and IFRS17.

2. Consistent modelling could reduce the probability / likelihood of accounting mismatches arising and leading to spurious volatility in the net financial results reported by insurer.

3. Future linkages for infrastructure and reporting purposes

» Impairment modelling is required for

assets classified at amortised cost of

FVOCI

» IFRS9 tool / model deployment

» Leveraging In-house and/or Vendor data

(i.e. PD, LGD engine)

» Derive the ECL at the granular level for

each exposure in the portfolio

IFRS 17

Top Down approach for the liability discount rate

Credit risk premium

for unexpected losses

Yield

Curve

based on

actual or

reference

portfolio

Expected credit

losses

Mismatch

IFRS 17

Discount

Rate

Methodology may

require PD and

granular LGD


IFRS 9 & 17 Thought Leadership Papers

Actuarial Models in an IFRS 17 World

Compliance with this standard promises to

bring the greatest disruption ever seen to

insurers’ financial reporting systems and

processes, by forcing companies to integrate

actuarial models deeply within reporting

processes.

Discount Rate Curves

In his IFRS17 Insight whitepaper, Nick

Jessop – Senior Director Research, decodes

the impact, significance and use of discount

curves in the IFRS 17 reporting process.

Getting IFRS 17 Implementation Right

InsuranceERM recently published a Q&A

interview with Moody's Analytics Analytics

that provides practical insights on some

aspects of the IFRS 17 implementation.

moodysanalytics.com/ifrs17

Level of Aggregation in IFRS 17

Massimiliano Neri shares his thoughts about

the level of contract aggregation required by

IFRS17 in his latest publication. Read his

whitepaper now to learn more about this core

requirement of IFRS17.

IFRS9 getting ready for the challenge

November 2018 will see the release of the

experiences learnt from IFRS9 banking

implementations and what this means for

Insurers.

Impact for Asset Managers

Appendix


» 100+ IFRS9 ECL

engagements in Europe,

Asia and Americas (exclu.

Americas)

» Working with institutions of

various sizes and

sophistication levels sizes

(Tier 1 to New banks)

» Banks, Development

Banks, Corporates and

Insurers

Moody's Analytics Analytics experience


Moody's Analytics and Thomson Reuters

joint SPPI solution

700K

Fixed Income

Securities

1M

Mortgage Backed

Security Pools

300K

Asset Backed

Securities

The partnership merges two core competences in one solution, offering the most competitive

approach in the market in terms of instrument coverage and functionality.

Clients will benefit from more than 30 SPPI related value and transparency fields and

documentation within one auditable solution delivered by Moody's Analytics Analytics and

Thomson Reuters.


ImpairmentStudioTM for Insurance CompaniesA CECL/IFRS9 orchestration platform to ensure a well governed and efficient period end process for

the new impairment accouting standards

Model Inventory for in house or

Moody's Analytics provided models

Scenario library with Moody's Analytics

documented and validated

methodology

Analysis project repository to run

production and ad hoc analysis

Full set of reports and disclosures to

support the analysis and review process

SUPPORT FOR

Risk, finance and

accounting analysts

Moody's Analytics, Internal

and external models

Scenario weighting

Attribution analysis

Audit tracking at loan level

Full disclosure set

Accounting entries (GL and

loan level)

Q-factor analysis support

for review challenge

SOC 1 Type 2 from Big 4

Interest Rate Risk in the Banking Book, Sep 2017 36

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