SAS KPMG Alliance Risk Management

Risk Management is a core strength for SAS Acknowledged leader in Risk Management Solutions Deployed in 50+ countries by 1,400+ organizations Top 3 vendor for the 7th consecutive year (2016) Ranked as a category leader for: Enterprise Stress Testing Systems (2015) Risk Data Aggregation and Reporting (2016) Model Risk Management Systems (2014) Credit Risk Analytics Solutions (2014) Solvency II Solutions (2014)

KPMG’s Risk Analytics Practice KPMG’s Risk Analytics practice provides professional services to complement the SAS ECL risk platform and solve our client’s challenges around their CECL transition. Service areas include: Project Strategy Systems and Data Modeling and Validation Accounting and Reporting

SAS – KPMG Alliance KPMG is a SAS Gold Partner working together in the area of Risk Management KPMG is the exclusive risk services partner for the SAS ECL platform SAS works with KPMG together globally to solve ECL challenges related to US GAAP CECL, IFRS 9, and other regulatory and risk challenges KPMG and SAS have been jointly working on CECL since 2017 and share numerous joint wins in the marketplace

Current Expected Credit Loss (CECL) CECL Overview New US standard for Credit Impairment Accounting Current Expected Credit Loss (CECL) ASU 2016-13 Financial Instruments - Credit Losses (Topic 326): Measurement of Credit Losses on Financial Instruments Issued by the Financial Accounting Standards Board (FASB) in June 2016 Replaces ASC 450-20 (FAS 5) and ASC 310-10 (FAS 114) Defines rules for credit loss allowance estimation Applicable to all banks, savings associations, credit unions, and financial institution holding companies Principle-based Guidance Effective from 2020

Current Standards (FAS 5, FAS 114) CECL Overview Some key changes CECL (ASC 326-20) Current Standards (FAS 5, FAS 114) Incurred Loss Approach Expected Loss Model Probable loss threshold No probable loss threshold Losses expected to incur over the next 12 months Lifetime expected losses from day 1 Forecasts over loss emergence periods “Reasonable and supportable” forecasts + reversion to historical information Wide latitude for judgmental adjustments based on qualitative and environmental factors Increased granularity of judgmental adjustments on front end – including assumptions on economic projections

Collaborative framework Key Requirements By Major Stakeholders Risk Model templates Scalability Transparency Development to production speed – time to market Reduce key man risks Finance Production workflow Controls and approvals Self-serve model What-if analysis Variance analysis Accounting Regulatory and management reports Automated accounting entries Auditability and impact analysis Flexibility Ease-of-use Processing speed Capacity Compatibility Implementation Time Security Governance and Control Reporting Collaborative framework Technology: leverage and enhance current technology assets - in a centralized, controlled, and scalable environment

SAS Expected Credit Loss Modular Approach Within Reporting & Analytics IFRS 9 / CECL introduce significant complexities and uncertainties: Required enhancements to models and processes Interpretation of principle-based standards Potential hits to income and capital Earnings volatility Necessitates coordination of work efforts in a highly controlled transparent environment: Model development Model execution & exploration Production & workflow Reporting & analytics Granular Data Analysis Advanced Analytics Disclosure Reporting Model Development Model Execution & Exploration Production & Workflow Modelling Time Series Forecasts PD, LGD, EAD Prepayments Loss Rates Exposure Models Macro Economics Model Deployment & Scenario Definition Workflow Administration ECL Calculations Aggregation & Adjustments Stage Allocation Accounting Posting Model Management Sensitivity Analysis Process Governance Reference Data Portfolio Data GL Data Third Party Data Scenario Data Collateral Market Data

SAS Expected Credit Loss Powerful Model Implementation Platform Business Challenges Inefficient execution Resource-intensive Multi-period projections Lack of adequate control Solution Highlights Execute loan level models Interactive selection and execution In-memory processing Why SAS? Quick, streamlined model implementation Simplified execution Advanced technology Key Points Efficient Model Management Transparency and Control High-speed Analytics

SAS Expected Credit Loss Reducing time and risk of implementation Requirements and Prioritization Review Accounting Policies Review Models & Methodology Define Governance & Controls Review Data Requirements High-Level Architecture Review and validate install in the defined environments. Define data structure. Define standard rules definition and terminology of processes, model, ETL, GL reconciliation. Define staging and allocation rules. Implement models for one portfolio. Define roles and workflows for review and adjustments. Sprint 1 Focus: Incorporate improvements identified in the first sprint in order to get a stable end-to-end solution. Implement remaining models. Integrate model outputs into workflows and comprehensive reporting. Testing and re-runs. User acceptance tests and Identification of defects. Sprint 2 Focus: Finalization of the models, disclosure reports, and rollout. Address unresolved issues identified in first two sprints. Provide training documentation and support. Sprint 3 Assessment 10

SAS Expected Credit Loss Key Benefits Your Challenge SAS Expected Credit Loss Resource constrained and already substantially invested in Basel and Stress Testing infrastructure Reduces implementation time and cost Leverages existing data mart and modeling processes Simplifies implementation of complex ECL estimation process 1 Implementation must conform to heightened governance regime Consolidates and Manages workflow Workflow and governance tools aid process management, instill strong controls, and improve auditability 2 Principle-based regulations lack implementation specifics PROVIDES Flexible and transparent environment Open modeling architecture is adaptable to changing interpretations and demands 3 Need an efficient and sustainable process to complete complex ECL estimates within time-constrained production cycles Creates a high-performance allowance process Distributed processing power provides extremely fast computations Centralized management coordinates all process activities 4

Software and Services for CECL KPMG and SAS Software and Services for CECL SAS data mart & data management tools Source, transform, and load relevant CECL data from any number of data warehouse and origination systems Model Implementation Platform (MIP) Configure and edit model implementation templates for each portfolio and set of models needed for ECL calculations Enhanced time to production and runtime environments for CECL models Model execution and production automation Manage production process flows with security and auditability Results review and effective challenge Dashboard views to review model outputs Manage qualitative adjustments and allocation Accounting and reporting Create journal entries and CECL disclosures Update management dashboards, views, and details reports Data sourcing and data quality controls Assess data sources, quality, controls, and lineage Create source-to-target mapping, data ETL jobs Model design, development, and implementation Champion, challenger, and benchmark model design Model estimation and calibration Model implementation an prototyping Model execution and production automation CECL process flow, internal controls, and second line review Results review and effective challenge Model output review and supporting analysis Model design and limitation documentation Accounting and reporting Accounting policy and SOX control documentation Deficiency remediation and CO attestation

CECL Change Assessment 7 Step Process KPMG and SAS utilize a seven-step approach for change assessment, which includes a readiness diagnostic. The results of this diagnostic allows us to focus the remaining activities on those areas that are most critical to the success of the project. The outcome of the overall assessment phase will facilitate optimal structure of the subsequent design and implementation phases of the CECL transition.

CECL Change Implementation Leveraging the gap analysis and information obtained during the assess phase, the project next moves into the design phase. KPMG and SAS work together to assist the Bank in designing appropriate accounting policies, operational practices, models, and systems & data management approaches in order to create CECL compliant loss projections. This will be the basis of governance and business requirements designed for CECL loss forecasting, and inform the implementation choices made in the final phase.

CECL Change Design KPMG and SAS will assist the Bank in implementing the CECL compliant practices scoped in the assessment and design phases. Using SAS’ proven phased approach for implementation, an initial end-to-end configuration is executed on a representative set of portfolios. This pilot is then expanded to include all relevant portfolios, followed by refinement and testing prior to a final CECL go-live.