1. SAS KPMG Alliance
Risk Management

2. 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)

3. 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

4. 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

5. Current Expected Credit Loss (CECL)
CECL Overview
New US standard for Credit Impairment Accounting
Current Expected Credit Loss (CECL)
ASU 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
(FAS 5)
and ASC
(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

6. Current Standards (FAS 5, FAS 114)
CECL Overview
Some key changes
CECL (ASC )
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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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.

14. 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.

15. 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.