Freddie Mac Single-Family Data Governance

April 26, 2016

© Freddie Mac 2

About Freddie Mac

© Freddie Mac 3

Chartered by Congress in 1970 to provide liquidity, stability, and affordability to the U.S. housing finance market

Operates three main business lines: » Single-Family (1 to 4 units) » Multifamily (5 or more units) » Investments

Participates in the secondary mortgage market » Buys mortgages and related securities » Issues guaranteed mortgage-related securities » Offers an array of products to fit market needs

Provides resources and tools to support customers and educate the public

Corporate Background

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Delivering Results

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Performance at a Glance

Metric In 2015

Total liquidity provided $402B SF liquidity provided $351B SF homes financed 1.6M SF refinances $198B

910K borrowers SF borrowers helped to avoid foreclosure

94K 2009-2015: 1.2M

Treasury draws1/ Dividend payments

$0/$1.7B Since 11/08: $71.3B/$98.2B

1Excludes the initial liquidation preference of Freddie Mac’s senior preferred stock of $1B. Dividend payments do not offset prior draws.

$5.8 billion in comprehensive income in 2015

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Book Year % SF Portfolio Seriously Delinquent Rate1

% of 2015 Credit Losses

Core book 2009-2015

66 0.21% 3

HARP + other Relief Refinance

18 0.75% 8

Legacy book 2008 and prior

16 4.12% 89

Total 100 1.32% 100

Building a Strong Book of Business

1Per MBA’s National Delinquency Survey, rate on first-lien SF loans in U.S. market was 3.95% as of 6/30/2015

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Single Family Portfolio & Workouts

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Expanding Homeownership Responsibly

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Better Solutions: Coming Soon - Loan Advisor Suite

Reliability Instill trust that

our tools are available when you

need them

Helping our customers succeed through an innovative suite of solutions designed to deliver certainty, efficiency, reliability, and usability

Certainty Give you confidence

that the loans you originate meet the

requirements for delivery and sale

to Freddie Mac

Efficiency Lower the cost to originate via automated data validation and focus your attention where it needs to be

Usability Provide intuitive and easy-to-use tools, with clear and actionable feedback

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Improving Loan Manufacturing Quality with the Integrated Loan Advisor SuiteSM

Application Processing / Underwriting Pre-Closing Closing Loan Delivery Servicing

Loan Prospector ®

Loan Collateral Advisor

Loan Quality Advisor ®

Selling System

Loan Coverage Advisor ®

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Giving Greater Certainty with Data and Tools

Uniform Data Standards

Uniform Loan Delivery Data Set Data elements required at loan delivery for Freddie Mac loans.

Uniform Appraisal Data Set Standardizes definitions and includes all required fields for an appraisal submission.

Uniform Closing Data Set Standardizes data associated with closing transaction: supports CFPB’s Closing Disclosure form.

Uniform Loan Application Data Set Leverages URLA and standardizes data for loan application submission.

Third

-Par

ty D

ata

Verif

icat

ion

Comprehensive Support

Data Quality Credit Quality Collateral Value/Quality Product Eligibility Risk Management

Pre-Purchase Loan Prospector ®

Assists lenders in manufacturing Freddie Mac eligible loans (credit and capacity). Home Value Explorer®

Provides collateral valuation for residential properties and can help flag potentially inflated appraised values during appraisal review. Uniform Collateral Data Portal®

Provides an early view into appraisal quality and any potential collateral risk. Loan Quality Advisor®

Helps lenders validate that a Freddie Mac eligible loan was manufactured and is ready for delivery.

At Purchase Selling SystemSM

Prices and purchases loans from lenders based on agreed to terms.

Post-Purchase Loan Coverage Advisor® Establishes, tracks, and analyzes loan events throughout the life of a loan impacting Seller representation and warranty relief dates. Quality Control Information Manager

Secure web-based reporting system for Freddie Mac and our customers to share performing and non-performing loan data and better manage repurchase and remedy requests.

Freddie Mac Suite of Tools

Data Validation

Gre

ater

Cer

tain

ty

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The Data Governance Journey

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A Historical Perspective

Data Certification based on agreed-upon procedures

SOX Attestation for financial statements and control testing

SOX CDE Identification; Full E2E Data Lineage; Full E2E DQ Controls supporting SOX Attestation

Rollout to Business; adopt Industry standard Governance Framework; Implement policies & standards; implement tools

Risk-Based Capital Rule

2014 Strategic Initiative

SOX/SEC Registration

Credit Crisis

IT previously managed the data governance organizations.

Regulatory Risk-Based Capital Rule required data certification.

The credit crisis caused serious delinquency and foreclosure volumes to spike; while mortgage servicers re-tooled to implement high volumes of new loan workouts, the risk of data errors increased.

Finance mandated an enhanced process for the identification of Critical Data Elements (“CDEs”), E2E data lineage maps and E2E transparent, comprehensive and testable data quality control design and execution.

In 2014, we started the full-scale implementation of Data Governance to support strategic business initiatives. Transferred data governance out of IT to the Single Family business.

© Freddie Mac CONFIDENTI AL 14

Single-Family Business Data Capabilities Framework

Data Governance

Technology Engagement

Reporting, Analytics

& Modeling Data Quality Metadata

Data Asset Mgmt Master Data Mgmt

SF DG&M Management

Vendor Management

Innovation

Data Capabilities Framework

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How Will the SF Data Strategy Generate Value? Empower. Simplify. Reuse. Control.

• Supports speed to market and innovation • Provides authoritative data and

robust toolsets • Catalogs source of truth for SF data

• Improves process efficiency and quality • Creates capacity • Reduces hard costs

• Standardizes “rules of the road” • Aligns risk mgmt with risk exposure • Reduces data sprawl & testing costs

Empower

Simplify / Reuse

Control

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Organization O

ur

Rol

e O

ur

Team

Deliver quality Single Family information and make it accessible and useful to the business

XX FTEs utilizing the following critical skills: Strategic analysis, communication skills, business analysis, data & research analysis, project management, relationship management, operations management, Metadata, risk & and control measures, understanding of Shared Information Assets (SIAs) mgmt

Contact the team members below for more information:

Vice President Jodi Morton

Data Governance Director

Loretta Ibanez

Business Intelligence &

Analytics Director Dennis Tally

Counterparty Data & Administration

Director Lori Smith

Approved Provisioning Points

(APPs) Manager Van Lin

Shared Information Assets Business

Operations Director Matthew Klena

CDW Manager Marjorie Sterner

ODS, RDS, DTS, EIH & BDMS

Glossary Manager Sudha Aluri

Func

tiona

l Te

ams

Area

s of

Res

pons

ibili

ty

Key Business Partners: Single Family, Finance, IT, Enterprise Architecture, Enterprise Risk Mgmt, Corporate Compliance, Privacy Office, Internal Audit, I&CM

Data Governance Business Intelligence & Analytics

Counterparty Data & Administration

Shared Information Assets Business

Operations*

• Design and implement SF data governance framework & strategy

• Manage SF Business Advisory & Governance Forum (“SF BADG”)

• Design publish, rollout & train on SF business Data & Metadata standards and procedures

• Design, implement & manage SF Metadata Repository

• Design, implement & publish SF Data Quality reports, dashboards & certification

• Manage SF Data & Metadata waiver/exception process

• Monitor SF data quality corrective action as appropriate

• Develop, deliver & communicate the SF Data strategy

• Develop and sustain SF Business Intelligence (BI) & Analytics Enclave (AE) multi-year roadmaps

• Manage the implementation of new SF analytic capabilities in conjunction with key business partners

• Provide assurance that SF Data strategy & roadmap align with SF strategic priorities and target state architecture

• Establish communication plans & organizational processes to support SFDG&M data consumption to drive increased value of data

• Lead, as required, and support SF and corporate research & development projects and initiatives; including Counterparty Approved Provisioning Points (APPs)

• Establish, manage and implement corporate & divisional strategy and administration activities including:

o Mgmt Rptg to key stakeholders & Sr. Mgmt

o Compliance with divisional & Corporate policies (Privacy, BCP, Records Mgmt, etc.)

o HomeFront & SharePoint content management & administration

• Full life cycle management for Shared Information Assets (SIAs)

• Manage data access to the SIAs & APPs

• Data Quality (DQ) controls monitoring and data validation support for SIAs & APPs

• Analyze, approve & validate data changes and corrections to SIAs

• DQ Issue & Resolution Mgmt support including reporting & communication of DQ issues & the availability of SIAs & APPs

• Risks & Controls mgmt, including audit support for SIAs & APPs

• Governance of all SIAs & APPs initiatives/enhancements including:

o Review of requirements o UAT o Post Prod results validation • PortVal

* Includes APPs, CDW, ODS, RDS, DTS, EIH & BDMS Glossary

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E2E Data Quality Framework

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Fit-For-Purpose Governance

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Governance Rollout Strategy

Non-SOX High Risk & Supporting Moderate SF Processes & Models:

Facilitated Rollout

2015

High Risk Models/ Processes 4 Models XXXX(H) XXXXX(H) XXXXX(H) XXXXXX(H)

5 Processes XXXXX(H) XXXXXX(H) XXXXXXX(Downgrad

ed to Moderate) XXXXXXX (H) XXXXXXXX(H)

2016 Projects: Follow Change Management

Processes For New SF Capabilities

2015-2016 SF Data Governance Standard Roll-Out Scope

Review project documentation to assess alignment with the SF Data Governance Standard

SF DG&M reviews projects that meet at least one of the following criteria: ‘Critical’ (i.e. >$500K) and

impacts a High Risk process in GRC, or

‘Critical’ (i.e. >$500K) and deemed Architecturally Significant

Conduct Training: Schedule classroom sessions

every eight weeks through June 30, 2016. 3Q and 4Q frequency TBD.

SF Data Governance Standard:

Update Standard and Conduct Training

Update v1.1 of the Standard targeted for 2Q

Waiver Process Execution EDMC DCAM Framework

adopted Conduct Training: Classroom sessions

every six weeks through June 30, 2016. 3Q and 4Q frequency TBD

Training addresses Standard alignment and project implementation

Additional Data Lead training focusing on Data Standard implementation in 2nd half of year

2016

New High Risk and Supporting Moderate Risk Models/ Processes 1Model: XXXXXX(M) XXXXXX (Addressed in 2015)

13 Processes XXXXXX(H) XXXXXX(H) XXXXXXX(H) XXXXXXX(H) XXXXXXXX(H) XXXXXXX(H) XXXXXXX(M) XXXXXXX(M) XXXXXXXX(M) XXXXXXXX(M) XXXXXXXXX(M) XXXXXXXX(M) XXXXXXXXX(M)

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Governance Rollout Process

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Highlights & Issue Descriptions

Notes: a) Data Quality controls will be marked as green unless there is a control deficiency or the de minimis threshold was broken. b) Issues are reported on an exception basis displaying the number of controls with issues identified in the current reporting month out of the total number of controls. c) Controls may have consumers with varying Fit-for-Purpose classifications, therefore may be counted in multiple columns. Controls will only appear within one Data Supply Chain business area.

Data Quality Performance Highlights All Fifty-two (XX) data quality controls and checks being

tracked performed as expected. These controls cover XXX of XXX SOX CDEs and XX of XXX Operational CDEs.

Twenty-eight (XX) of the SOX controls also cover Operational CDEs for High Risk Consumers. These controls cover XX of XXX Operational CDEs.

One additional key control is being tracked as of XXX reporting cycle. (Key Control Description(X.XX.XXX.XXX)

DQ Control Issues & Notes No data quality issues exceeded the de minimis thresholds

for XXX results

Data Quality Performance

Dat

a Su

pply

Cha

in (C

ontr

ol O

wne

r)

Con

sum

e

Finance

MM&R

DAVM

ERM

Stor

e

SF CDM

Port Val

CDW

Acqu

ire

Default Fees & Claims

REO & CL

Servicing

Sourcing

Performing Loans

SOX Total: XX

High Total: XX Moderate

Operational

Fit-for-Purpose (Data Consumer) Not Tracked/Unavailable

x/n Moderate Issue

x/n Critical Issue

0/n On-Track/No Issues

n = Number of Controls x = Number of Controls with Issues

SOX Key Control performance and certain loan-level Non-Key Control performance over SOX and Operational CDEs displayed. Controls have been added to the dashboard as applicable, with additions incorporated as available. Control mapping for Operational Non-SOX CDEs isongoing.

Data Quality Dashboards: Data Quality Performance Control results as of XXX, 2016

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Data Quality Performance – High Risk Consumers

Dat

a Su

pply

Cha

in (C

ontr

ol O

wne

r)

Con

sum

e

Finance

MM&R

DAVM

ERM

Stor

e

SF CDM

Port Val

CDW

Acqu

ire

Default Fees & Claims

REO & CL

Servicing

Sourcing

Performing Loans

Model 1 Total: XX

Model 2 Total: XX

Model 3 Total: XX

Model 4 Total: XX

Process 1 Total: XX

Process 2 Total: XX

Process 3 Total: XX

Process 4 Total: XX

Process 5 Total: XX

High Risk Models (Consumer) High Risk Processes (Consumer) Not Tracked/Unavailable

x/n Moderate Issue

x/n Critical Issue

0/n On-Track/No Issues

n = Number of Controls x = Number of Controls with Issues

Notes: a) Data Quality controls will be marked as green unless there is a control deficiency or the de minimis threshold was broken. b) Issues are reported on an exception basis displaying the number of controls with issues identified in the current reporting month out of the total number of controls. c) Controls may have consumers with varying Fit-for-Purpose classifications, therefore may be counted in multiple columns. Controls will only appear within one Data Supply Chain business area.

Control performance over Operational CDEs displayed. Controls have been added to the dashboard as applicable, with additions incorporated as available. Control mapping for Operational Non-SOX CDEs is ongoing.

Data Quality Performance – High Risk Control results as of XXXXX, 2016

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The Metadata Management Business Case

What is Metadata?

How will we use it?

Why do we need it? Business Metadata Technical Metadata

Metadata

Operational Metadata

Information about the technical components such as:

Table Name

Column Name

Data Type

Links to related files and indexes

Descriptive information providing context about the data, typically defined by the business such as:

Purpose of the data

Data Owner

Data Classification

Definition of data population

Business rule logic, purpose and

intentAdministrative information to manage a resource:

Creation Date, File Type

Access Rights/Entitlement Restrictions

Retention Rules / Purging Rules

Every day, new analytics, reports and models are being generated in response to changing market, cost and regulatory environments.

As a starting point for the Analyst, seldom does a data glossary exist and is accessible via a central source/ interface (data glossary = metadata or information describing key data in business terms).

Every day, Analysts resort to using their social network and word of mouth to find the right data – as a result 40% or more of their t ime is spend finding and fixing the data. This approach is tedious and oftentimes yields inconsistent results.

The data glossary underlying this data is often viewed as a personal asset (i.e., job security) vs. an enterprise asset.

In spite of attempts to leverage personal networks to understand the data, the Analyst must often re-work his/her data because stale or incorrect data / metadata was used.

The result – too much analyst t ime is spent on low value activities. Analyst t ime is often allocated…40% finding, fixing, reworking data40% developing business insight20% explaining the results.

Failing to find all the data they need from existing sources, new extracts may be developed to supply the missing data. This extends the turnaround times, increases cost to the business, adds complexity and decreases responsiveness to change.

Often no one owns and maintains the metadata/ data glossary relating to the new data pulled - so again the data will not be re-used.

$

$

$

Where is the right data?

Metadata

Metadata

Metadata

$ $ $ $

Analyze and Develop Insight

Design Build Test O perate

...Somewhere insideFreddie Mac Today…

I’ll have to create a new

extractTime spent on data detective work

40%

Explain Results

20%

40%

Search Business Term

View Metadata and Custom Attribute

Related Terms (Relationship View)

Run / View Lineage

Relate Logical Term to Physical Term Attribute

View Lineage

Impact Analysis

View Glossary Functionality

Glossary Hierarchy (Based on Capabilities Model Options)

Policy (Relationship View)

Workflow Propose Change to Metadata

Business Glossary Desktop

andAnalyst

Metadata Manager

Analyst

Tool Interface Key Functionality

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Informatica Metadata Implementation

Plan, Requirements, RFP, Software Deployment, Operating Model Cross-Divisional Project Team

Glossary Design & Load

Technical Scanning & Loading

Lineage Linking & Viewing

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Data Governance is a business activity with technology enablers: approach it from a business risk management perspective

Implementation is a journey

Don’t try to boil the ocean

Lead with process then select integrated technology solutions

Know your supporters and detractors and have a strong, focused communication strategy to drive adoption

Partner with influential, willing participants

Don’t just be the data cop: offer a carrot, in addition to having a stick

Get all three lines of defense aligned on the problem, desired outcomes and solutions

Lessons Learned