Moody s Analytics IFRS 9 for Insurers. Roshni Patel and Nadja Roos Wednesday, 26 September 2018

Transcription

1 Moody s Analytics IFRS 9 for Insurers Roshni Patel and Nadja Roos Wednesday, 26 September 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,

3 Agenda Key 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,

4 Accounting Standard Timelines IFRS9 General adoption IFRS17 adoption* IFRS9 exemption for Insurers CECL - SEC Filers CECL - Public Co. s CECL - Other Co. s * IFRS 17 general adoption. Early adoption possible. Some regulators might decide to accelerate timelines. IFRS9 for Insurers, London, September 26,

5 Stages to IFRS9 compliance Small steps achieving greater affect IFRS9 for Insurers, London, September 26,

6 Implementation Timeline an Insurance view Gap assessment / Impact Study Methodology development / enhancement IFRS 9 compliance tactical approach Q Q Q Q Q Q Diagnostic Phase Business Model and SPPI tests IFRS 9 compliance tactical approach IFRS 9: Automation and Workflow Integration with IFRS17/Solvency II IFRS9 for Insurers, London, September 26,

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 Q / early Q 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,

8 1 Advantages of early Implementation

9 Advantages of early Implemetation Best Practices Incorporating lessons learned Accountability / Responsibility Institutions identifying a Super user early on in the implementation project saw considerable synergies. Governance Strong Project Governance with Senior Management involvement and clear escalation procedures made projects more efficient from the start. Validation / Audit Early engagement of internal validation and external audit teams brought everyone on the same level simultaneously and focused the formal process. Global / Local Local Workshops with detailed user training across the end to end calculation process. IFRS9 for Insurers, London, September 26,

10 Advantages of early Implemetation Best Practices Modelling Scenarios Explore the use of internal macroeconomic scenarios for consistency with Stress Test / ICAAP. PD / LGD Use internal LGD models for consistency if available. Incorporate conditional PiT PD / LGD term structures into existing accounting systems. Consistency Consistency with internal process (Credit process, watch list approach, ICAAP, Stress Testing). Validation Incorporating new models in existing operating model (e.g. expanding current Validation Framework). IFRS9 for Insurers, London, September 26,

11 Advantages of early Implemetation Internal communication and Incorporating results Parallel Run Opportunity for a longer parallel run phase. IFRS9 Impact Analysing the impact of IFRS9 and determining potential mitigants (credit review of stage 3, determining impact of staging in stress scenarios, QoQ volatility). Internal Communication Providing Senior Management (CEO, CRO, CFO, CTO) with frequent and tailored information especially around the QoQ volatility. 4 Infrastructure 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,

12 Advantages of early Implemetation Considering Operational Aspects of IFRS9 People Cost Resource Mix» 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) 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) Operational Cost» Data enhancing Risk and Finance data» Personnel cost» Methodology changes, re-calibration annually and assumptions reflections 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) IFRS9 for Insurers, London, September 26,

13 2 Portfolio Coverage

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,

15 Business Model and Classification & Measurement BM and SPPI Test 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 Accounting Processes Revision 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 Implementation Advise on implementation of models, data requirements, policies & procedures for BM and SPPI Test Automation to perform BM and SPPI test IFRS9 for Insurers, London, September 26,

16 3 Impairment Modelling Requirements

17 Impairment Process Experience IFRS9 Data preparation, Data Load, ETL/Manual, Exception reporting, Data substitution, Rules Engine Data Management Portfolio Segmentation Ad hoc and/or pre-approved segmentation at any level of granularity CECL/IFRS9-allowable Methodologies and Models feeding these methodologies, Assumptions Methodology/ Models Selection Typical Process Steps Scenario Design/ Selection One or multiple Scenarios and their weights, Reversion (if desired) Model and Accounting Engines execution Model(s) & Accounting Execution Qualitative Adjustments Framework Management Override at any level, interagency and CECL qualitative factors, entityspecific 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 Publish and Extract (Data and GL) IFRS9 for Insurers, London, September 26,

18 IFRS 9 Impairment Calculation Steps An end-to-end process 1 Macroeconomic 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) 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 Stage 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 ECL 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 IFRS9 for Insurers, London, September 26,

19 Impairment Model Overview Macroeconomic Scenario Forecasts Scenario Probability Weights» Forecasts of GDP, unemployment, prices, etc.» Minimum 3 alternative scenarios: baseline, upside, downside» Business relevant scenarios» Expected probabilities of scenarios Macro-Conditioned, Point-in-Time, Forward- Looking Default Probabilities Loss Given Default Exposure Discount X at X = Factor Default Expected Credit Loss Credit Stage» Stage 1, 2, or 3 based on credit risk PIT Conversion» If TTC, need to convert to PIT Default Risk Measure» PD Model» External or internal rating IFRS9 for Insurers, London, September 26,

20 IFRS 9 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 IFRS9 for Insurers, London, September 26,

21 Macroeconomic Scenarios Probability-weighted expected loss Multiple Scenario Forecasts Derive probability-weighted ECL BL S0 S1 Baseline / Most Likely New Upside Scenario Stronger Near-Term Rebound 2007Q1 2010Q4 2014Q3 2018Q2 2022Q1 2025Q4 Scenario probabilities S2 S3 Mild Second Recession Deeper Second Recession 2012Q1 2014Q1 2016Q1 2018Q1 2020Q1 S4 Protracted Slump Baseline S1 S3 IFRS9 for Insurers, London, September 26,

22 United Kingdom IFRS9 Scenario Forecasting United Kingdom Real GDP, 2015 bil GBP S4 S S1 S BL United Kingdom Unemployment rate, % S4 S3 S1 S0 BL United Kingdom Inflation, % change yr ago United Kingdom House price, 2010= S4 S S1 S S4 S3 BL S1 S BL Sources: ONS, Moody's Analytics Analytics IFRS9 for Insurers, London, September 26,

23 Moody's Analytics Analytics IFRS 9 Proposal Expected Credit Loss Calculation Calculations steps 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,

24 What is the Rating to PD Converter? PD by Rating Country adjustment Pointin-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 Sector adjustment IFRS9 for Insurers, London, September 26,

25 IFRS 9 and beyond: Implementation challenges and what lies ahead Addressing the data challenges: LGDs Leveraging PD-LGD correlation when internal data is insufficient» 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 90% 80% 70% PD-LGD correlation PD-LGD correlation = 77% 6% 5% PD*LGD levels 60% 4% LGD 50% 40% 3% DR 30% 2% 20% 10% PD-LGD correlation = 50% 1% 0% % Default Rate LGD Loans LGD Bonds IFRS9 for Insurers, London, September 26,

26 Stage Allocation Criteria Absolute Criteria PD Master Scale Future Default Rate under each scenario 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 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 Portfolio Distribution Watch list account Decrease in collateral coverage ratio Possibility to segment by model, rating scale or industry IFRS9 for Insurers, London, September 26,

27 4 IFRS17

28 Connecting IFRS 9 and IFRS 17 Consistent credit modelling / Infrastructure IFRS 9 Modelling impairments IFRS 17 Top Down approach for the liability discount rate» 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 Yield Curve based on actual or reference portfolio Expected credit losses Credit risk premium for unexpected losses Mismatch Methodology may require PD and granular LGD IFRS 17 Discount Rate 1. Insurers are looking for consistent approach to credit modelling across both sides of the balance sheet i.e. IFRS 9 and IFRS 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 IFRS9 for Insurers, London, September 26,

29 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. 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 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 IFRS9 for Insurers, London, September 26,

30 5 Q&A

31 Appendix

32 Moody's Analytics Analytics experience» 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 IFRS9 for Insurers, London, September 26,

33 Moody's Analytics and Thomson Reuters joint SPPI solution 700K Fixed Income Securities 300K Asset Backed Securities 1M Mortgage Backed Security Pools 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,

34 ImpairmentStudio TM for Insurance Companies A CECL/IFRS9 orchestration platform to ensure a well governed and efficient period end process for the new impairment accouting standards 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 Analysis project repository to run production and ad hoc analysis Full set of reports and disclosures to support the analysis and review process Model Inventory for in house or Moody's Analytics provided models Scenario library with Moody's Analytics documented and validated methodology IFRS9 for Insurers, London, September 26,

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