IFRS 9. Challenges and solutions. May 2016

Transcription

1 IFRS 9 Challenges and solutions May 2016

2 REGULATORY CONTEXT and objectives of the document Additional document on Impairment Nov 2009 Mar 2013 IFRS 9 Final Standard BIS Guidelines Guidance on accounting for expected credit losses Feb 2015 IFRS 9 Effective date GO LIVE Jan 2011 Jul 2014 Jan 2018 ED (Exposure draft) on Impairment Focus of the document ED Financial Instruments: Expected credit loss IFRS 9 General Framework Classification and measurement Impairment («LLP») Hedge Accounting View CRIF Show CRIF approach about specific aspects of the new Impairment methodology Share a possible implementation masterplan 2

3 NEW IMPAIRMENT IFRS 9 MODEL Main differences between IFRS 9 and IAS 39 The new Standard for the evaluation of expected losses provides a model characterized by a perspective view, able to immediately detect the expected losses over the lifetime of a financial instrument. This determines the immediate recognition of expected losses and that the estimates must be continuosly updated in order to consider any changes in the credit risk of the counterparty. Stream from IAS 39..to IFRS 9 EVOLUTION Staging Approach Risk Measures Portfolio segmentation Identification of the loss Loss time horizon Not explicitly provided (but it is possible to segment the portfolio for collective write-downs purpose) Incurred loss, calculated on the basis of objective evidences, It is not allowed including effects of future events One year (as practice) 3 stage, function of credit risk (and relative increase over time) Expected loss, calculated on the basis of past events, current conditions and reasonable and supportable forecast One year or lifetime Forward looking component Not requested Requested (expected changes in the macroeconomic scenario) 3

4 STAGING APPROACH The definition The new Impairment model, called «Three-Bucket («Stages») approach», provides a classification of the financial instruments in scope based on three different groups For each bucket, a different methodology of estimation for the expected loss is provided Deterioration Low risk/no deterioration Significant increase of risk Impairment event Stage 1 1 year Expected loss (EL1Y) Stage 2 lifetime expected loss (LEL) Stage 3 lifetime expected loss (LEL) As soon as a financial instrument is originated or purchased, 12-month expected credit losses are recognized in profit or loss and a loss allowance is established For financial assets, interest revenue is calculated on the gross carrying amount (ie without adjustment for expected credit losses) If the credit risk increases significantly and the resulting credit quality is not considered to be low credit risk, full lifetime expected credit losses are recognized The calculation of interest revenue on financial assets remains the same as for Stage 1 If the credit risk of a financial asset increases to the point that it is considered credit-impaired, interest revenue is calculated based on the amortized cost (ie the gross carrying amount adjusted for the loss allowance) Lifetime expected credit losses are still recognized on these financial assets Improvement 4

5 IFRS AND IRB MODELS Risk measurement of CRO and CFO The expected loss framework adopted by the IFRS 9 is close to the one use for IRB purpose Adopting an IRB modelling could be advantageous in order to: Possible synergies (PD or LGD estimation) Increase of the consistency of the data used by the bank Need to develop two different types of parameters (PD, LGD, EAD...) for accounting purposes and for regulatory capital calculation, drawing from a common matrix that allows to reconcile and identify any anomalies. Measurement of loss low CRO perspective (lifetime) CFO perspective (Stage 2) CFO Perspective (Stage 3) Riskiness evolution CRO perspective (1 year) CFO perspective (Stage 1) high 5

6 PREVIEW OF THE MAIN IMPACTS Impacts and main phases to be monitored The compliance with the IFRS 9 requirements, although sufficient in terms of regulatory compliance, will change the credit value chain, from strategy to processes. Here below the main phases to cover during an IFRS 9 project: DETECTION MEASUREMENT VALUATION First Time Adoption classification PD Lifetime LGD accounting Forward Looking Cash flow analysis Provisioning CRO-CFO communication IMPACT ON PROCESS (RISK MGMT, CREDIT VALUE CHAIN, ACCOUNTING) AND IT SOLUTIONS Focus 6

7 CLASSIFICATION Transfer Criteria The migration between stages is determinied by the change (from the origination date to the reporting date) of the rating or other indicators related to the counterparty credit risk. It is therefore necessary to define the transfer criteria between stage that allow continuous monitoring over time.. MIGRATION FACTORS MARKET INDICATORS (eg. Interest rates and unemployme nt) TRIGGER AQR (eg. DSCR, forbearance) RATING (internal and external ) ECONOMIC AND FINANCIAL INDICATORS (es. revenues reduction, asset quality, leverage) 7

8 CLASSIFICATION Transfer Criteria The choice of indicators to be used as transfer criteria requires evaluation of the following aspects SEGMENTATION LOGIC Differentiation of trigger event by risk segment, counterparty and product TRIGGER IDENTIFICATION Structured analysis of a high volume of internal and external information in order to define an initial set of indicators DISCRIMINATORY POWER ANALYSIS Correlation analysis of each trigger possible variable with the effective increase in the credit risk SET OF THE THRESHOLDS Determination of a threshold based on a historical analysis of the triggers and the risk profile QUALITAIVE ANALYSIS Involvement of business people for an integrated assessment that is not the expression of only quantitative elements Avoid unnecessary volatility of the impairment system that can have unexpected and significant impact on the value adjustments and then on the income statement as well as compromising the ability to ensure good governance of credit 8

9 FIRST TIME ADOPTION Initial detection of the risk Initial detection In the first adoption of the IFRS 9, it will be necessary to determine the credit risk for all exposures at the origination date through the use of the best information available without undue cost and effort Exemptions Low risk exemption Performing Sub-performing Non-performing Stage 1 Stage 2 Stage 3 Performance in line with the expectation Significant deterioration respect to the origination Evidence of impairment E(L) 1 Year E(L) Lifetime 9

10 FIRST TIME ADOPTION Impacts on LLPs and capital LLPs under IAS 39 IFRS 9 Incurred loss LCP other effects E(L) IRB EL lifetime PD point in time forward looking LLPs IFRS 9 In first time adoption it will be necessary to monitor the impacts on the income statement and indirectly at capital level. Shortfall under IAS 39 (IRB banks) IFRS 9 For IRB banks, also, changes in the stock of LLps will result in a direct impact on the capital, through the shortfall between adjustments and expected losses. CET1 Shortfall (E(L) LLPs) CET1 after shortfall New shortfall under IFRS 9 CET1 under IFRS 9 10

11 PD LIFETIME Determination of the lifetime default curve Two possible approaches Markov Chain Vintage Markov approach Vintage approach Medium/High risk curve Low risk curve Medium/High risk curve Low risk curve Main Features Time from the origination Easier lifetime PD definition «Memorylessness» hypothesis Mean reverting ( More conservative for low credit risk exposures) Time from the origination A lower level of conservatism for high duration portfolios and concentrated in the best rating classes (loans) It needs long time series A third approach can be mixing Markov Chain and Vintage Curve Using bureau data, can support the Bank in the construction of Lifetime Benchmark curve 11

12 PD LIFETIME Determination of the lifetime default curve It s possible to estimate Markov Matrix embedding forward looking effect for the first 3 years and then hook for next n years Vintage Curves In the chart below we can see how Vintage Curve analysis can be more favorable compared to pure Markov, translated in a PD reduction. PD 12,0% 10,0% 8,0% 6,0% 4,0% 2,0% Markov + Vintage Puro Markov Rating «low risk» PD 40,0% 35,0% 30,0% 25,0% 20,0% 15,0% 10,0% 5,0% Markov + Vintage Rating «high risk» Puro Markov 0,0% ,0% Ageing (years) Ageing (years) 12

13 LGD FOR IFRS 9 PURPOSE LGD Basel IRB vs LGD IFRS 9: our view The LGD used for IFRS 9 purpose will be slightly different from the one used for Basel IRB internal model, here below the main differences among LGD Basel IRB and LGD for IFRS 9 purpose are provided Features From Basel to IFRS 9 Eligibility criteria for Guarantees /collateral Only if suitable («eligible») Wider Choice Recovery Costs To be incorporated into the LGD estimate (direct & indirect) To be excluded (to avoid double counting) Downturn factor Present Not present (connected to the forward looking scenario) Floor on secured exposures Discounting rate Included Not defined prescriptively. Excluded Effective interest rate (EIR) 13

14 ACCOUNTING LGD Stage 2 Sub-performing For exposures belonging to the Stage 2 also the LGD is measured in lifetime perspective. This implies the need to consider the impact of the collaterals on the loss, correlated with the increase vintage of the credit REAL ESTATE LOANS Residual amount A decrease in the residual debt amount implies - ceteris conditions an increase of loan to value ratio This effect leads to a progressive reduction of LGD that often occurs precisely on time horizons affected by an increase in the marginal PD. LTV (%) Vintage (years) Banks able to "capture" in their own models this kind of effect will be able to get benefits in terms of lower provisions in the income statement. LGD (%) Vintage (years) Vintage (years) 14

15 FORWARD LOOKING VALUATION Key points IFRS 9 specifically requires to quantify the significance increase of credit risk (migration to the Stage 2) using also of forward looking information, aimed at capturing the foreseeable future economic environment Historical relationships between macroeconomic variables (eg. unemployment) and default rates or expected loss Default rate Forecast scenario Unemployment rate Forward looking estimation GDP 5 months rate Unenemployment Inflation 10years rate Statistic model (es. Error Correction Model) Macroeconom ic variable s Credit risk Loss rate Historiacal Worst Best Expect ed Years 15

16 IMPACTS ON OTHER PROCESSES The new accounting standard IFRS 9 will have a significant impacts on some "core" processes of the bank and in some important phases of the Credit Value Chain. In particular: Update of risk parameters Forward looking estimation Transfer criteria Credit Strategy redefinition New structure of Lending Powers Credit monitoring evolution Risk Based Pricing update Product catalogue review Budget review and performance evaluation Gap Analysis of the Data model Data Quality IT architecture evolution 16

17 IFRS 9: A PERVASIVE IMPACT Impacts on Credit Value Chain CREDIT VALUE CHAIN IMPACT DESCRIPTION 1 Credit Policy Defining credit strategy will be relavant estimate staging transition (from st1 to st2) Target portfolio is going to be simulated in a multi-year perspective 2 Monitoring & EW Improvement of Early Warning System, building a dashboard of KPI preventing staging migration to st2 3 Price Origination Development of Lifetime Risk Based Pricing Model based on IFRS 9 components and embedding forward looking scenarios. 4 Strategies & Decision Mgmt Deliberative body and rules will consider effects on Expected Loss and Stage Migration 5 Governance e Reporting Reporting system, from operational to directional side, will included effects of risk parameters and staging criteria 17

18 IMPLEMENTATION OF IFRS 9 Application Diagram (illustrative) Application Diagram Credit Value Chain 3A 3A 1B Pricing engagement Origination Early warnning & Monitoring Risk Management 1A «Curves» simulation engine (ex. SAS) 2 Stage «assigning» engine Accounting $ IAS IFRS Engine DWH / legacy 18

19 WORK PLAN Masterplan of implementation (illustrative) CRO Area Workstreams Risk management Parallel Analysis, requirement running Go Live III IV I II III IV I II «Lifetime» models (PD, Lgd, Ead) Bucket analysis - transfer criteria Forward Looking estimation Tuning Tuning Tuning III CFO Area Accounting Classification and First time adoption Simulation of impacts Tuning Tuning CLO Area Credit Processes Risk measurement and validation Provisioning Regulations Regulations Credit Value Chain IT & Data governance Risk Procedure and Simulation Tool Credit Processes and procedures Information Framework and data governance Governance & PMO 19

20 Thanks for your attention For further informations please contact: Marco Macellari Claudio Sangiorgi Service Line Leader Credit Risk & Regulatory Advisory Senior Account EMEA Markets