Early challenges of CECL i mplementation Hosted by: Todd Pleune, - PowerPoint PPT Presentation

Early challenges of CECL i mplementation Hosted by: Todd Pleune, Protiviti Protiviti Presenters: Xiaojing Li, CoStar Group Matthew Murphy, State Street Information Classification: General

Meet the Webinar presenters… Moderated by: Matthew Murphy Xiaojing Li VP, Lean Strategy Director, Quantitative Consultant Methods State Street CoStar Group Todd Pleune Managing Director, Model Risk Managaement Protiviti Information Classification: General

Senior Management and Board Support Important to establish and maintain appropriate “tone at the top” • Implementation involves many bank areas; may necessitate organizational changes Reporting Scenario Forecast Governance CECL ALM Risk Management Data Collection and Planning and Regulatory Oversight Storage Budgeting • Timeline should support “no surprises” objective for this important change – Full-year parallel run – Incorporate lessons learned in IFRS9 implementation • Maintaining momentum flagged by many as a top challenge – C-suite support, with dedicated Project Management Office and Steering Committee – Leverage both internal and external working groups Information Classification: General

Initial Modeling Decisions What loss forecasting capabilities do you have in house today? • Consider existing frameworks to minimize process differences – Harmonize with pre-existing forecasting processes - CCAR, DFAST, IFRS9, etc. – to the extent possible – Ultimate solution should be practical, operable, well-controlled and beneficial – Must account for model bias • Weigh trade-offs – lower complexity vs. reserve accuracy, higher complexity vs. auditability, up-front costs vs. ongoing maintenance, higher reserve vs. higher volatility, automation vs. judgment, etc. – Solutions that require material downstream data needs may prove difficult to implement – Allow for feeder model validation and regular re-calibration • Fundamental decisions can have long-term implications – Reasonable and supportable period – Mean reversion techniques – “Reasonably expected” Troubled Debt Restructurings – Timing of recoveries Information Classification: General

Data Gaps Process complicated by merger of loan origination data with Accounting and Risk data sets • The sooner gaps are addressed, the quicker an institution can begin compiling historical data − Auditors understand that some historical data may not be obtainable, but they expect a plan to be in place to correct gaps over time • Existing data streams must be subject to data quality assessment • Existing systems may have limitations for storing of additional static data − Anticipate delays and resistance to changes in data capture and storage Information Classification: General

Other Model Challenges One size does not fit all; must maintain flexibility in approach • Certain asset classes – e.g., long duration securities and loans – can be problematic when trying to measure “life of asset” expected credit losses − Reserve process expanded to include assets beyond loans − Complete balance sheet review to ensure all assets are accounted for • Must also decide on optional use of multiple scenarios ₋ Basic assumptions must be disclosed ₋ Reliance on market consensus insufficient, as consensus is historically unreliable in period leading up to economic contraction ₋ Must also establish a framework for weighting scenarios ₋ Requires regular updating, which can be expensive • Despite increased sophistication of loss forecasting, must maintain qualitative factor element to model • Stress-testing and back-testing may help identify model characteristics that may produce unintended consequences ₋ May also help in setting investment and lending strategies ₋ Different portfolio segments will demonstrate different levels of volatility Information Classification: General

Process Controls Higher data requirements necessitate “production environment” approach • Must consider sufficiency of existing governance − Some in-scope assets covered by different teams or committees − Model sophistication may necessitate a higher level of review and challenge − Multiple scenarios adds complexity • New process will be subject to considerably more scrutiny than existing process − Regulators − Internal and External Auditors − Model Validation • Must also consider specific regulatory requirements ₋ How will you ensure SOX compliance? ₋ Policies and procedures should be clear on roles and responsibilities of all stakeholders • Greater automation will allow for more frequent, and timely, loss estimations ₋ Monthly estimation will help minimize quarter-end surprises Information Classification: General

Disclosure Requirements Not a last priority • Disclosures in advance of adoption (SAB74) • Ongoing qualitative elements − How loss estimates are developed − Factors that influence estimate - past events, current conditions, forecast(s), etc. − Risk characteristics of each portfolio segment − Changes in policies and impact of same − Asset purchase and sales • Roll-forward of allowance • Vintage (presenting amortized cost by year of origination) • Past dues and non-accruals • Collateral Dependent Financial Assets • Difficult to assess market standard for disclosure prior to adoption ₋ Markets may demand more than what you have prepared for disclosure ₋ Should consider timing of when disclosures must be available Information Classification: General

CoStar Risk Analytics Early Challenges of CECL Implementation The ASU requires an organization to • gather relevant historical data measure all expected credit losses for • Identify data gap in historical Historical financial assets held at the reporting information date based on historical experience, Experience • Identify loss drivers based on current conditions, and reasonable and historical experience supportable forecasts. Financial institutions and other organizations will now use forward-looking • identify data gap in current Current information to better inform their information credit loss estimates. Conditions • build an efficient data collecting and updating system • select macro-variables Reasonable • select local market variables The ASU requires enhanced disclosures and • build a forecast model for loss drivers to help investors and other financial Supportable • build a forecast model for losses statement users better understand Forecasts significant estimates and judgments used in estimating credit losses, as well as the credit quality and underwriting standards of an organization’s portfolio. • full transparency Enhanced • automatic report Disclosure • Interactive review Source: http://www.fasb.org/jsp/FASB/FASBContent_C /NewsPage&cid=1176168232900 Information Classification: General

CoStar Risk Analytics CECL Road Map – Data is the Key Overcome the challenges down the road Historical Credit Loan Market & Risk Economi Data Segment Model c Data Data Historical Current Loss Qualitative Loan Econometri Data Adjustmen Data Scenari c Model t o Analysis Current Macroeconomic Loss Driver Expected Forecast Forecast Credit Loss Data Model Reporting & Documentatio System Communicating Governance n Information Classification: General

CoStar Risk Analytics Historical Experience Gather relevant historical data and identify data gaps in historical information q Historical Loan & Collateral Data q Historical Loan Default & Loss Data • Loan current balance • Delinquency history • Origination date • Timing of default • Origination term • Default type • Remaining term • Outstanding balance at default • Remaining Amortization term • Workout or modification • Remaining IO term • Reinstate Status • Coupon rate • Timing of loss • Rate reset • Loss type (FC/REO/DPO/Note sale…) • DSCR (origination & contemporaneous) • Outstanding balance at loss occurrence • LTV (origination & contemporaneous) • Liquidation expense • Prepayment penalty structure • Liquidation proceeds • Re-underwriting parameters • Prepay activity • Credit enhancement • Property address • Property type • Multi-property or cross-collateralized • Property value • Net operating income • Cap rate Historical Experience • Tenants • Leases • …… Information Classification: General

CoStar Risk Analytics Historical Experience Identify loss drivers based on historical experience Developing A Sound Model with Continuous Performance Tracking q Variable Selection Process § Data availability § Significant relationship with credit risk and losses § Improve model performance (accuracy and discrimination power) 85% ROC (Receiver Operating Characteristics) Attribution Incremental from Vintage 83% 81% Incremental from Loan Size 79% 77% Incremental from Region 75% 73% Incremental from IO Type 71% Incremental from Seasoning 69% 67% ROC Power from LTV & DSCR 65% Apartment Office Retail Warehouse Hotel Source: CoStar Risk Analytics (Asof 2017Q4) Information Classification: General