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.
IFRS9 for Insurers, London, September 26, 2018 3
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
IFRS9 for Insurers, London, September 26, 2018 4
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.
IFRS9 for Insurers, London, September 26, 2018 5
Stages to IFRS9 complianceSmall steps achieving greater affect
IFRS9 for Insurers, London, September 26, 2018 6
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
IFRS9 for Insurers, London, September 26, 2018 7
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?
IFRS9 for Insurers, London, September 26, 2018 9
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.
IFRS9 for Insurers, London, September 26, 2018 10
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).
IFRS9 for Insurers, London, September 26, 2018 11
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.
IFRS9 for Insurers, London, September 26, 2018 12
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
IFRS9 for Insurers, London, September 26, 2018 14
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.
IFRS9 for Insurers, London, September 26, 2018 15
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
IFRS9 for Insurers, London, September 26, 2018 17
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)
IFRS9 for Insurers, London, September 26, 2018 18
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
IFRS9 for Insurers, London, September 26, 2018 19
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 =
IFRS9 for Insurers, London, September 26, 2018 20
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
IFRS9 for Insurers, London, September 26, 2018 22
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
IFRS9 for Insurers, London, September 26, 2018 23
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
IFRS9 for Insurers, London, September 26, 2018 24
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
IFRS9 for Insurers, London, September 26, 2018 25
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
IFRS9 for Insurers, London, September 26, 2018 26
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%
IFRS9 for Insurers, London, September 26, 2018 27
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
IFRS9 for Insurers, London, September 26, 2018 29
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
IFRS9 for Insurers, London, September 26, 2018 30
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
IFRS9 for Insurers, London, September 26, 2018 33
» 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
IFRS9 for Insurers, London, September 26, 2018 34
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.
IFRS9 for Insurers, London, September 26, 2018 35
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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