GARP Webcast Series

February 2016

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Modeling Platform for the “Future” Analytical Banker Today

Martim RochaMartim Rocha, Advisory Business Solution Manager, SAS

Martim Rocha is currently an Advisory Business Solution Manager at the Risk and Quantitative Solutions Division at SAS. In this role, he focuses on the topics of stress testing, IFRS9, capital planning and risk and finance integration.

Martim has more than 20 years of experience in business analytics and data management. He has designed and managed projects for banks, insurance firms and other financial services companies in areas such as finance management, risk management, predictive analytics, financial and sales performance, strategy management, and customer analysis and segmentation. In addition, he has taught courses on advanced decision support systems, data warehousing and data mining at the Autonomous University of Lisbon and at the ISCTE Business School.

Before joining SAS, Martim was a partner on the Business Analytics focused consulting firm, Noscitare and worked in financial services companies.

Martim is Post-graduated in Business Administration from Nova SBE and Graduated in Computer Science from ISIG.

Dr. Venkat VeeramaniVenkat Veeramani, PhD, SVP, Head of Quantitative Analytics, Wintrust Financial Corporation

Venkat is an Enterprise Risk Management professional experienced in areas that span from Risk Strategy, Risk Appetite, Credit and Market Risk Management to Financial Modeling. Venkat currently oversees the bank’s risk and financial modeling and quantification efforts.

He is well versed with all areas of Enterprise Risk Management including Risk Identification, Measurement, Mitigation, Management, Validation and Reporting. He has previously worked at Morgan Stanley, Discover Financial Services (Spin-off from Morgan Stanley) and HSBC. Venkat is a published author on articles related to game theory and risk management which were presented at numerous industry and trade conferences at both national and regional levels. Venkat holds a Ph.D. in Ag Economics and an M.S. in Economics from the University of Kentucky.

Agenda• Overview of Landscape – Dr. Venkat Veeramani• Background and Context – Martim Rocha

• Drivers for change and action• Major Challenges• Recent stress-testing survey

• Integrated Risk Platform• The analytical lifecycle• Comprehensive architecture with a modular approach

• Data Management• Model Development & Management• Model Implementation• Consolidation and Reporting

• Conclusions

Landscape of Modeling Platforms and Data Systems

Higher levels of computing power, ease of access to information and innovations by financial disruptor firms are forcing the industry to develop real-time modeling solutions sitting on top of real-time data systems.

Real-time integrated Modeling and Data Systems offers significant benefits.

• Higher levels of efficiency• Holistic view• Comprehensive solutions• Faster decision making capability• Unlocks unknown Risk vs

Reward spectrum• Greater levels of transparency• Cheaper in the long run

Benefits do come with a cost• Requires heavy infrastructure

investment• Requires upgrade to legacy

processes and procedures• Requires resources with

advanced analytical skillsets

Silos Batch Integrated

Real-time Integrated

Mode

ling

Plat

form

s

Data Management Systems

Silos

Batch Integrated

Real-time Integrated

.

.

Modeling Platforms: Are you playing Catch-up?

6

Financial Technology (FinTech) disruptor firms are changing the interactions within and between financial institutions, while the traditional financial institutions are weighed down by legacy systems, processes and regulatory burdens.

Time

• Speed • Advanced Analytics• Personalization

Current

Current trend requires modeling platforms to be able to fluidly access data from different sources and produce comprehensive business solutions in real-time. A few buzzwords are:

• Ease of Use• Customer Engagement• Convenience

• Flexibility• Efficiency• Transparency

Data, Data and More Integrated Data

7

Meeting both Business and Regulatory requirements hinges around the availability of reliable data.

Model predictions are only as good as data.

Regulatory Requirements (i.e., DFAST, CECL, CFBP)

Business Requirements

Integrated Modeling Platforms

Background and context

Drivers for change and actionThe Regulatory push

Modelling Platform

BCBS239 Risk Data

Aggregation and Reporting

Basel III Capital Requirements and Liquidity

Recommendation A4 of

ESRB/2012/2Funding Plans

IFRS9/CECL Expected Credit

Loss

EBA, CCAR, APRA Regulatory

Stress-testing

10

New Requirements / New Challenges

Model-based risk and capital management

Data Collection

New InformationIndividual Account Level Forecasts / HistoricalSegmentation Individual Asset Level

= Massive Amount Data = More Granular Data

Forward Looking Calculations

Financial Impact Increased Measurement complexity Additional Data Collection More Risk Models

= New Analytical Models

Governance

Documentation Governance Change Control Regulatory Capital forecast Model Management

= New Control Framework

Moving from modeling focused on IRB and on model qualification in the statistical sense (building statistically correct atomic models, testing their predictive power, monitoring their performance). Associated with a low number of “risk models” (order of 10-100)

Future emphasis is in impact analysis on full portfolio performance (end measure), be sure that the correct atomic models are in place and get used, speeding the deployment. Future Overall model population can reach 1000 units.

Further ChallengesData discrepancies that require added reconciliation effort

IT systems are organized by departments – each uses it’s own coding, grouping, hierarchies, time reference, …Combined data doesn’t match, added effort for reconciliation and data quality

Cross reporting happens only in very aggregated levels, losing important detail and in many cases hiding problems that could otherwise be addressed pro-actively.

To overcome the difficulties on combining data, reporting is done in very aggregated levels – limits effectiveness

Difficulties on accurately calculate risk adjusted measures at the right level of detail, making decision around which products and which regions to invest almost only based on gut feeling.

The world is flat but each region, demographic group, provided service has its particular characteristics – the devil is in the details as well as the return

Undermine any attempt of creating a framework of predictive analytics due to the lack of historical integrated information.

To anticipate the future you should look into the past, the more detailed you have on the past the best estimate you do on the future

Reduce drastically the number and depth of scenarios analyzed for planning.The cumbersome and manual based processes you have today take too much time for each analysis you do thus limiting the scenarios you analyze

Recent stress-testing survey

• Manage granular level data

• Process large volume of calculations

• Consolidate Scenario Based Data

• Orchestration of firmwide Analysis

• Model Inventory• Model Validation• Model Review

• Data Quality• Data Lineage• Metadata Communication

Managing DataMonitoring

Model and Risk Performance

ImplementationCoordination

Key Areas of Readiness for future

changes in Stress Testing

Stress Testing: A View from the

Trenches, GARP, Sep 2015

For more details, please refer to the

SAS – GARP webinar

Integrated Risk Platform

The analytical lifecycle

DATAPREPARATION

DATAEXPLORATION

BUILDMODEL

VALIDATEMODEL

DEPLOY/ EXECUTE

MODEL

EVALUATE /MONITORRESULTS

Domain ExpertMakes DecisionsEvaluates Processes and ROI

BUSINESSMANAGER

Model ValidationModel DeploymentModel Monitoring Data Preparation

IT SYSTEMS /MANAGEMENT

Data ExplorationData VisualizationReport Creation

BUSINESSANALYST

Exploratory AnalysisDescriptive SegmentationPredictive Modeling

DATA MINER /STATISTICIAN

IDENTIFY ISSUES /

ADJUSTMENTS

POST RESULTS

Comprehensive architecture with a modular approachEnterprise Risk

Governance

Model Development Consolidation and Reporting

Aggregation and Allocation

Results Data Repository

Parameters

External Market Data

Collateral Transactions

GL Data

Modeling Workbench

Inventory of Models

Risk & Finance Data Collection, Quality assurance and Standardization

Rules, Metrics,Dynamic Hierarchies

Stress Testing Data Mart

Model Implementation

Model Management

Data and Model StagingData Validation and Aggregation

Portfolio Data

Data Sourcing

Validation and Governance

Scenario Management and Model Execution

Implementation Platform

Model Specification,Estimation and Calibration Regulatory and Management Reporting

3rd Party Data

Data Management

Enterprise - One centralized place to collect all data, assure quality, standardize, reconcile and distribute

Governance – full control on what data is used for what

Consistency – Prepare data to feed into different risk engines, keep track of data used – One version of the truth

Results Data Repository

Parameters

External Market Data

Collateral Transactions

GL Data

Inventory of Models

Risk & Finance Data Collection, Quality assurance and Standardization

Rules, Metrics,Dynamic Hierarchies

Stress Testing Data Mart

Data and Model StagingData Validation and Aggregation

Portfolio Data

Data Sourcing

3rd Party Data

Comprehensive architecture with a modular approach

Model Development & Management

Enterprise – Use whatever language is convenient for model development but manage all models under the same framework/application

Governance – Model Inventory for enhanced governance on what model is being used with what results, why we have the model, who has developed, who has tested it, is still performant

Consistency – One process for Model development, Model calibration, Model validation, Model approval

Governance

Model Development

Modeling Workbench

Model Management Validation and Governance

Model Specification,Estimation and Calibration

Comprehensive architecture with a modular approach

Model Implementation

Enterprise - One consistent approach to manage scenarios and model execution

Governance – full control on which scenario was used for which run with which data, which model and produce which results

Consistency – Prepare data and scenario to feed into the different risk engines

Performance – Cutting-edge technology on model execution (in-memory, grid parallel processing)

Model Implementation

Scenario Management and Model Execution

Implementation Platform

Comprehensive architecture with a modular approach

Enterprise – One place for aggregation and consolidation of results including workflow to coordinate tasks and people interaction

Governance – Full control on how the results are generated, path from results to model, to data, to scenario

Performance – lower time to deliver, one place for orchestration able to trigger detailed calculation and aggregate results – quick refresh for iterative simulation

Consolidation and Reporting

Consolidation and Reporting

Aggregation and Allocation

Regulatory and Management Reporting

Comprehensive architecture with a modular approach

Conclusions

• Key Functionalities• Model Execution from a

Single Model Inventory • Scenario Management –

Regulatory & Ad hoc• Risk Engine - Multi-horizon

capabilities by scenario at Loan Level

• Value Drivers• Ability to implement the

most advanced modeling suites in the industry

• Reduce the time to develop models and using them in Production

• Simulation Capabilities for stress testing and beyond

Comprehensive architecture with a modular approach

Process to Aggregate and consolidate results

• Key Functionalities• Process orchestration with

status and timeline• Support for regulatory

cycles • Result consolidation,

reconciliation and aggregation

• Report generation • Regulatory and filing views

• Management• Ad-hoc

• Value Drivers• Process efficiency,

transparency • Better governance practices

for regulatory and internal scrutiny

• Leveraging process for Business Purposes and go beyond compliance only

Comprehensive architecture with a modular approach

Q & A

Creating a culture of risk awareness®

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About GARP | The Global Association of Risk Professionals (GARP) is a not-for-profit global membership organization dedicated to preparing professionals and organizations to make better informed risk decisions. Membership represents over 150,000 risk management practitioners and researchers from banks, investment management firms, government agencies, academic institutions, and corporations from more than 195 countries and territories. GARP administers the Financial Risk Manager (FRM®) and the Energy Risk Professional (ERP®) exams; certifications recognized by risk professionals worldwide. GARP also helps advance the role of risk management via comprehensive professional education and training for professionals of all levels. www.garp.org