#1SLFRS 9 Financial Instrument Presenter: Rajith Perera EY Building a better working world#2Classification of Financial Instrument#3SLFRS 9 Financial Asset Classification Model Debt (including hybrid contracts) Derivatives 'Contractual cash flow characteristics' test - SPPI (at instrument level) Equity Pass 'Business model' assessment Fail Fail Fail Held for trading? (at an aggregate level) 1 Hold-to-collect contractual 2 cash flows BM with objective that results in collecting contractual cash flows 3 Neither (1) Yes No nor (2) and selling FA Conditional fair value option (FVO) elected? No No Amortised FVOCI cost (with recycling) Yes FVPL No FVOCI option elected? Yes FVOCI (no recycling)#4Classification & measurement Key changes to LKAS 39 Equity securities Debt instruments (including loans and hybrid contracts) Debt Instruments Fair value option Embedded derivatives Measured at FVPL unless entity decides to present fair value changes in OCI (No. Recycling) New measurement categories classified on the basis of: The contractual cash flow characteristics of the instrument The business model under which those instruments are held Recognition of Expected credit losses in P&L Slight change in scope as instruments failing the SPPI test and instruments managed on a fair value basis are at FVPL per default Derivatives embedded in financial asset hosts are no longer separated#5Classification & measurement What remains the same? Financial assets and liabilities held for trading Financial liabilities Fair value option Embedded derivatives Measured at FVPL. Financial instruments held for trading include derivatives Classification and measurement for financial liabilities. Financial liabilities are measured at: Amortised cost Unless At FVPL (if held for trading, designated or managed on a FV basis) Condition of the presence of an accounting mismatch remains the same Accounting for hybrid contracts without a financial asset host remains the same#6Options An overview - Classifying Financial Asset Business model Held within a business model whose objective is to hold financial assets in order to collect contractual cash flows Held within a business model whose objective is achieved by both collecting contractual cash flows and selling financial assets Financial assets which are neither held at amortised cost nor at fair value through other comprehensive income Conditional fair value is elected Contractual Cash Flow Characteristics Test Pass Fail Amortised Cost FVPL FVOCI (debt) FVPL FVPL FVPL FVPL N/A Option elected to present change in fair value of an equity instrument not held for trading in OCI N/A FVOCI (equity)#7Example 2 Financial institution holds investments to collect their contractual cash flows. The funding needs of the entity are predictable and the maturity of its financial assets is matched to its estimated funding needs. The entity performs credit risk management activities with the objective of minimizing credit losses. In the past, sales have typically occurred when the financial assets' credit risk has increased such that the assets no longer meet the entity's documented investment policy. In addition, infrequent sales have occurred as a result of unanticipated funding needs. Discuss on the most suitable business model? EY#8Example 2 - Solution Although the entity considers, among other information, the financial assets' fair values from a liquidity perspective (i.e. the cash amount that would be realized if the entity needs to sell assets), the entity's objective is to hold the financial assets in order to collect the contractual cash flows. Sales would not contradict that objective if they were in response to an increase in the assets' credit risk, for example if the assets no longer meet the criteria specified in the entity's. documented investment policy. Infrequent sales resulting from unanticipated funding needs (e.g. in a stress case scenario) also would not contradict that objective, even if such sales are significant in value. [SLFRS 9.B4.1.4 Example 1]. EY#9Example 3 A financial institution holds financial assets to meet its everyday liquidity needs. The entity seeks to minimize the costs of managing those liquidity needs and therefore actively manages the return on the portfolio. That return consists of collecting contractual payments as well as gains. and losses from the sale of financial assets. As a result, the entity holds financial assets to collect contractual cash flows and sells financial assets to reinvest in higher yielding financial assets or to better match the duration of its liabilities. In the past, this strategy has resulted in frequent sales activity and such sales have been significant in value. This activity is expected to continue in the future. EY#10Example 3 - Solution The objective of the business model is to maximise the return on the portfolio while meeting everyday liquidity needs and the entity achieves that objective by both collecting contractual cash flows and selling financial assets. In other words, both collecting contractual cash flows and selling financial assets are integral to achieving the business model's objective. [SLFRS 9.B4.1.4C Example 6]. EY#11Impairment/Expected Credit Losses#122008 2009 2010 1% of Risk weighted assets to the Total Assets 1 https://www.cbsl.gov.lk/en/statistics/statistical-tables/financial-sector 2011 2012 2013 2014 2015 2016 2017 2018 0.6 0.5 0.4 % of Risk weighted assets to the Total Assets TETTE Increase in Advances (compared to previous year) Increase in Gross NPA (compared to previous year) 0.00% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 20181 2019* ** -100.00% Q1... There is a need for the Banks to increase their focus towards strengthening their Risk Management Practices & Systems such as credit appraisal, underwriting & monitoring processes which will result in increased profitability & strengthening the capital. An increasing trend in the banks exposure towards assets with high risk has been noticed which implies that Banks will need to invest in capital optimisation techniques The Provision Coverage Ratio of the Banks has been deteriorating year on year on account of increase in the NPA Increase in NPA which would also result in increase in the lifetime ECL once the Banks adopts SLRFS Current State Analysis - Banks Statistics within the Banking industry were analysed to identify key trends in profitability and capital management 100.00% Key Takeaways NPA has increased rapidly during 2017 and half year 2019 which requires further strengthening the monitoring process and implementation of an Early Warning Signals framework#13Summary of expected credit loss model: general model Initial recognition Allowance: Criterion: (with exceptions) 'Performing' 12-month expected credit losses 'Under-performing' 'Non-performing' Lifetime expected credit losses The credit risk has increased significantly since initial recognition Interest revenue based on: Gross carrying amount Objective evidence of impairment Gross carrying amount Net carrying amount Change in credit quality since initial recognition improvement deterioration#14What does "Significant” mean? Type of product Interpreting << significant » depends on several factors Original credit risk at origination A given PD variation in absolute terms is more significant for assets with better quality at inception Expected maturity The probability of default increases with maturity Qualitative indicators Which can then be translated into PD levels 60 Corporate- PD (%) PD - Corporates 50 S&P rating 1 Y 5 Y 10 Y AAA 0.00 0.36 40 0.76 40 AA 0.02 0.39 1.03 30 30 A 0.07 0.59 1.71 BBB 0.20 1.73 3.93 20 20 BB 0.71 8.05 14.04 10 B 5.10 22.04 28.87 0 CCC/C 26.85 46.74 51.13 AAA AA A BBB BB B CCC/C 1 Year Note: Standard and Poor's Global Corporate Average Cumulative Default Rates by Rating Modifier (1981 - 2012) 5 Year - 10 Year#15How Accounting Standard Looks at It Why 90 Days? Can we rebut 90/30 days presumption? Mostly Applied Many Jurisdictions apply 90days to perform Risk Management Activities Yes Provided you have evidence to prove that default does not trigger on the 90th day. "The IASB acknowledges that defining the backstop as 90 days past due is arbitrary, but it considered that any number of days would be arbitrary and that 90 days past due best aligned with current practice and regulatory requirements in many jurisdictions. Page 15 "It was also noted that the purpose of the rebuttable presumption is not to delay the default event until a financial asset becomes 90 days past due, but to ensure that entities will not define default later than that point without reasonable and supportable information to substantiate the assertion" How relevant the measurement of credit risk using DPD 0110100 www 9149010106 Can 90 days help entities better manage the Risk? 010010100101 8011001100101001010011 in Weak imant Number of Days past due is a lagging indicator to Ascertain Credit Risk 1100 Yes. Can be used as an early warning indicator 80101001 EY#16Simulating the Rebuttal of Default Loan Balance Rs.1000 # Rentals Cash Inflow 30 Days 1 60 Days 90 Days 2 3 120 Days 4 150 Days 5 180 Days 6 210 Days 7 240 Days 8 270 Days 300 Days 9 10 100 100 100 100 Without Rebuttal for a Customer Aged 90Days Probability of Default is = 100% on 90 DPD Recovery After Default = 400 Therefore the Loss = 600 Resulting in a LGD of 60% (600/1000) Overall Loss Rate = = 100% (PD) x 60% (LGD) = 60% With Rebuttal for a Customer Aged 90Days Probability of Default is = 100% on 180 Days Therefore CF recoveries until T6 is Captured in PD Therefore the PD of 90 Days = 80% (8/10) Outstanding at the Default Point = 800 Recovery After Default = 200 Therefore the Loss = Overall Loss Rate Page 16 = 600 Resulting in a LGD of 75% (600/800) = 80% (PD) x 75% (LGD) = 60% EY#17Case 1 - Impairment Provisioning Approach Financial institution XYZ has following customers. Customer A - Financial institution recently granted a loan customer A who has an initial rating of 2. - Customer B This customer has a large manufacturing company in the Asbestos manufacturing industry. Company currently has a rating of 1. Customer C - This customer currently has a rating of 3. Last year he maintained a rating of 2. Other information Financial institution has a rating system of 1-5, Financial institution does not grant loans to customers with an initial rating of 3-5.#18Case 1 (Contd..) Additional Information Manufacturing industry relating Asbestos production is currently facing a business threat on possible closure of operation in 2018. Inline with Internal credit Risk management policies of the Financial institution a single notch downgrade in the rating. system Financial institution considers as a significant deterioration in credit risk. Requirement Determine whether Financial institution needs to assess 12 months ECL/Life time ECL for each customer. ▸ If the customer belongs to Life time ECL which performance bracket do they belong to? (Stage 2/3)#19Case 2 - Loan Product Segmentation for collective assessment Financial institution XYZ has following products with specified additional information. Loan product Term loans Overdrafts Leasing Housing Additional Information Recently industry analysis suggest that manufacturing and property development sector will be negatively impacted within the next 2-5 years. In line with the revised budget proposals LTV ratio is amended as follows. Three wheelers - 25% Cars - 50% Lorries/Trucks - 90% According to internal statistics the credit risk behavior of housing would vary based on LTV ratio. Determine the most appropriate segmentation#20Definition of Default/Credit Impaired AXH EY Building a better working world#21Measurement of expected losses#22Summary: Measuring expected credit losses Unbiased and probability-weighted estimate Best available information Information about Information about + past events current conditions Reasonable and supportable forecasts The time value of money#23Definition of 12-month and lifetime expected credit losses Lifetime expected credit losses Expected credit losses that result from all possible default events over the expected life of a financial instrument. 12-month expected credit losses The portion of lifetime expected credit losses that result from default events on a financial instrument that are possible within the 12 months after the reporting date. 'Default' Definition is not defined by the standard and there is a 90 days past due rebuttable presumption. In practice Although 12-month horizon may be consistent with regulatory capital requirements (e.g., Basel), the computation of expected credit losses under SLFRS 9 will differ from regulatory capital calculation.#24A generalised equation X ECL12m = PD12m × LGD12m × EAD12m × D12m 1 2 3 4 5 LT ECLLT = t=1 PDtX LGDtX EADt × Dt 1 4 PD ► Probability of defaulting in period t ▸ Expected to be unbiased, i.e. not down turn or best estimate ► Discount factor to discount cashflows (effectively in this case losses) to the reporting date ► Discounts at EIR 2 LGD 5 ► Forecasted economic loss if default happens in period t Expected to be unbiased ► Includes discounting at the EIR LT ► Summation of individual period (typically year) ECLs to arrive at lifetime ECL ► Required provision balance for stage 2 & 3 assets 3 EAD ► Projected exposure if default occurs in period t Behavioural payments capped at the contractual lifetime unless revolving Transition criteria ▸ Determine transition from stage 1 to stage 2 (or 3) ► Based on changes in default (not loss) likelihood since origination#25Delinquency rate ཚོ རཱྀ གཽ ཚོ ཚོ ཚོ ཚོ 5% 4% ECL Modelling Maturation Maturation profile 0% 0 1 2 3 4 5 6 7 Time on Book (Years) 8 9 10 11 12 -Avg % Delinquent 3% 1% 1% ༄། ཨཽ ༈ མཻ ། གཽ སྠཽ ༔ Delinquency rate Portfolio Decomposition Portfolio delinquency 5% 4% 3% 2% 1% 0% 0 1 2 3 4 5 6 7 8 Time on Book (Years) 9 10 11 12 -2001 -2002 -2003 -2004 Vintage Vintage Quality: 12m and 24m Delinquency Rate 0% 2001 2002 2003 2004 Vintage 12m 60m Relative conditions 2 1.5 1 0.5 -0.5 -1 -1.5 -2 0 Jan 01 Jun 03 Innovate to Win! Environment Environment (exogenous) Jan-04 Jan-05 Jan-05 lan 07 Date Jan G8 GQ CHRY Jan-11 Jan-12 Jan-13 Each product type will have their own maturation profile, typically reaching a plateau in default rates as the asset / population matures. The credit quality of different vintages will reflect the risk appetite, market conditions and lending standards in place at the time of origination. The remaining exogenous component reflects seasonality, economic fluctuations etc. Economic forecast scenarios can be directly applied. Page 25 EY#26Page 26 Key Modelling Aspects ECL calculation: 12-month and Lifetime LGD Segmentation Only default population for estimation Identification of population sub-segments where different models can be developed per segment (e.g. Secured/Unsecured, Modified/Non Modified exposures). LGD Estimation The two main parameters are: Loss Given Loss, which for secured exposures is an estimate of collateral liquidation, while for unsecured exposures, estimation is performed via cash payments of non- cured exposures. Cure Rate based on an estimated workout period, where segmentation or behavioural models are typically developed. Macroeconomic Adjustment Factors for secured lending include house prices, CRE prices, Typical macro economic factors for cure rate and unsecured lending. Indicative Data Needed Collateral information: < NPL facilities/Settlements CF Records Write-off's Deem Write-off Post-default payment behaviour Time to recovery Cash recoveries Haircut 18% Time-to-default 16% 1 LGD Loss given loss 14% Time to liquidation 12% HPI 2 Cure rate Costs Sep-15 Jan-16 SLFRS9 LGD term structure $!!! What is not Compliant Using regulatory LGD values without analyzing whether adjustments are required Amortizing Exposure (Linear) May-16 Sep-16 Jan-17 May-17 Sep-17 Jan-18 May-18 | Sep-18 Jan-19 | May-19 Sep-19 Jan-20 May-20 EY#27Treatment of limits and lifetime of revolving products AXH EY Building a better working world#28SLFRS 9 Requirements SLFRS 9 paragraph 5.5.19 The maximum period to consider when measuring expected credit losses is the maximum contractual period (including extension options) over which the entity is exposed to credit risk and not a longer period, even if that longer period is consistent with business practice. SLFRS 9 paragraph 5.5.20 However, some financial instruments include both a loan and an undrawn commitment component and the entity's contractual ability to demand repayment and cancel the undrawn commitment does not limit the entity's exposure to credit losses to the contractual notice period. For such financial instruments, and only those financial instruments, the entity shall measure expected credit losses over the period that the entity is exposed to credit risk and expected credit losses would not be mitigated by credit risk management actions, even if that period extends beyond the maximum contractual period EY#29Generalized calculation of forward-looking EAD Amortization method for closed-end product For amortization schedule of closed-end loan (e.g., mortgage), the principal is paid down over the life of the loan. There is an option to consider prepayment model to reflect behavioral maturity. EAD = Current Balance Principal Outstanding for closed-end loan 1,200,000 Credit conversion factors method for irrevocable commitment loan For irrevocable undrawn commitment, the entity shall measure expected credit losses over the period that the entity is exposed to credit risk and expected credit losses would not be mitigated by credit risk management EAD = Current Balance + CCF x undrawn portion Principal Outstanding for revolving product 1,030,000 1,000,000 800,000 600,000 1,020,000 1,010,000 1,000,000 990,000 980,000 970,000 нии 960,000 950,000 940,000 0 m 930,000 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 400,000 200,000#30CCF approach to estimate EAD 100% b% a% 0% Current Utilization Utilization at Default Potential risk factors in CCF estimation ► Type of obligor ► Relationship between the Financial institution and obligor in adverse circumstances ► Alternative sources of funds available to the obligor ► Covenants (which restrict future draw- downs in cases where the credit quality has declined) ► Historical payment difficulties Time to maturity b% - a% CCF = 100% - a% EY#31Forward looking considerations AXH EY Building a better working world#32Multiple scenarios versus most likely outcome Example: Approach 1: Most likely economic scenario considered Approach 2: Probability weighted average of plausible economic scenarios underpinning the central scenario Upside Scenario Scenario Scenario ECL unemployment likelihood (CU) 4% 20% 30 Central / most likely Downside 5% 50% 70 6% 30% 170 Probability weighted average 92 Responsiveness of credit outcomes to macro-economic factors is often non-linear meaning considering a single most likely outcome may not Standard's requirements for an unbiased probability weighted measure Potential approach I Model multiple outcomes using macro-economic regression models and take a probability weighted average ► Computationally intensive Requires judgement in determining and weighting scenarios Potential approach II Model a single outcome and apply judgemental adjustments to reflect differing future outcomes & non- linearity ▸ May not require regression models More judgement required and increased burden to justify outcomes EY#33Innovate Economic Factor Adjustment to Win! Quantitative & Qualitative factors have been considered for the assessment of Economic Factor Adjustment: Quantitative GDP Growth Qualitative Government Policies Status of the Industry Business Inflation Average LTV Interest Rate Exchange Rate Unemployment Regulatory Impact Publically available data on economic forecasts have been extracted from World Bank/IMF websites. Where ever reliable estimates were unavailable such economic conditions were forecasted using statistical methods. The multiple economic scenarios were considered with following probability weighted outcomes: Base Case Page 33 : 50% Best Case : 25% Worst Case : 25% EY#34Economic Outlook - Futuristic Based on Probability Weighted Multiple Outcomes Innovate to Win! GDP Growth Inflation (YoY) (CCPI) Interest Rate (AWPLR) 6% 6% 10% 5% 5% 8% 4% 4% 6% 3% 3% 4% 2% 2% 2% 1% 1% 0% 0% 0% 2010 2015 2020 2025 2014 2016 2018 2020 2022 2024 2012 2014 2016 2018 2020 2022 2024 250 200 150 100 50 Exchange Rate (US$:LKR) Unemployment 6% 5% 4% 3% 2% 1% 0% 2005 2010 2015 2020 2025 2005 2010 2015 2020 2025 Important Consideration Historical Behavior cycles of the economy was analyzed during the post war period, Mean reversal method have been considered to establish the behavior cycles based on the past trend based on the standard deviation/volatility To establish the worst case & best case historical moving averages of the standard deviation using the "Bollinger Bands Theory" was considered and applied a ceiling/floor to the worst case/best case Binomial Behavioral cycles/lattice cycles using expected values have been considered to replicate the future economic outlook Page 34 EY#35Probability Weighted Multiple Economic Scenario Probability Weighted Multiple Economic Scenario Basecase Forecast 50% GDP Growth 2016 4.50% 2017 2018 2019 2020 2021 2022 2023 Remarks 5.00% 5.00% 5.20% 5.40% 5.50% 5.50% 5.50% Inflation (YoY) (CCPI) 5.0% 5.3% 5.1% 5.0% 5.0% 5.0% 5.0% 5.0% Average LTV Interest Rate (AWPLR) 7.53% 7.53% 7.53% 7.53% 7.53% 7.53% 7.53% 7.53% Exchange Rate (US$:LKR) 145.80 152.33 159.16 166.29 173.74 181.52 189.65 198.15 Unemployment 4.96% 4.96% 4.96% 4.96% 4.96% 4.96% 4.96% 4.96% Best Case Forecast 25% 2016 2017 2018 2019 2020 2021 2022 2023 Remarks GDP Growth 6.82% 7.32% 7.32% 7.52% 7.72% 7.82% 7.82% 7.82% Inflation (YoY) (CCPI) 2.6% 2.9% 2.7% 2.6% 2.6% 2.6% 2.6% 2.6% Average LTV Std Interest Rate (AWPLR) 7.53% 7.20% Exchange Rate (US$:LKR) 147.26 148.73 Unemployment 4.00% 6.89% 150.22 4.00% 4.00% 6.59% 151.72 153.24 4.00% 4.00% 6.30% 6.03% 5.77% 5.52% 154.77 156.32 157.88 Deviation +1 4.00% 4.00% 4.00% Worstcase Forecast 25% GDP Growth Inflation (YoY) (CCPI) 2016 2.18% 7.4% 2017 2018 2.68% 2.68% 7.7% 7.5% 2019 2020 2021 2022 2023 Remarks 2.88% 3.08% 3.18% 3.18% 3.18% 7.4% 7.4% 7.4% 7.4% 7.4% Average LTV Interest Rate (AWPLR) Exchange Rate (US$:LKR) 7.53% 168.26 175.79 7.87% 8.23% 183.67 8.60% 9.00% 9.40% 9.83% 10.02% Std Deviation -1 191.90 200.50 209.48 218.86 252.57 Unemployment 5.10% 5.10% 5.15% 5.15% 5.44% 5.50% 5.55% 5.55% EY#36Individual Vs Collective#37Summary of expected credit loss model: general model Initial recognition Allowance: Criterion: (with exceptions) 'Performing' 12-month expected credit losses 'Under-performing' 'Non-performing' Lifetime expected credit losses The credit risk has increased significantly since initial recognition Interest revenue based on: Gross carrying amount Objective evidence of impairment Gross carrying amount Net carrying amount Change in credit quality since initial recognition improvement deterioration#38Case 4 Financial institution XYZ has 10 large corporate customers which amounts to approximately 40% of the total loan portfolio of the Financial institution. Requirement 1. Do you require to perform Individual Impairment for these 10 customers? 2. What will happen to customers who does not have an objective evidence of impairment? 3. What will happen to customers who has objective evidence of Impairment, but no Impairment provision? EY#39Interpretation and implementation issues in measuring expected credit losses In practice Reasonable and supportable information Interpreting the term 'undue cost or effort' Adjusting historical information to reflect current conditions and forecasts of future conditions (e.g., use of econometric model, base-case model, data used for budgeting and capital planning) Translating macroeconomic factors into expected credit losses Leveraging on calculation, stress testing and information used for Basel regulatory requirements#40Interpretation and implementation issues in measuring expected credit losses (cont.) In practice Discounting Interpreting the term 'approximation' of the effective interest rate Calculating the effect of discounting Collateral Including cash flows from the realisation of the collateral and other credit enhancements only if they are part of the contractual terms and not recognised separately#41Disclosures Objective: Enable users to understand entity's estimate of expected credit losses and changes in credit risk Reconciliation of opening and ending gross carrying amount and credit loss allowance or provision Financial instruments measured at 12-month ECL Financial instruments measured at lifetime ECL Financial instruments with objective evidence of impairment Credit-impaired financial assets Inputs, assumptions and techniques ► Collateral information ▸ Disaggregation by credit risk rating grades Write-off policy Assets evaluated on individual basis#42Questions & Answers 20 260 280 300 320 3 READ BEARING HERE 40