Boost your data accuracy with these 5 steps

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Boost your accuracy in data reporting
Speakers
Julie Moroschan, Consultancy Director, Experian
Julie has more than 20 years working with Metro 2® reporting format. She was an active member of the Consumer
Data Industry Association (CDIA) Metro 2® Task Force, responsible for developing, maintaining and enhancing the
Metro 2® format. In her former role, Julie created a formal enterprise-wide Data Standards and Governance team,
responsible for identifying and resolving data related issues, promoting data quality, ownership, awareness and
knowledge across the enterprise.
In her role in the Global Consulting Practice she has engaged with clients to improve the accuracy and integrity of
their reported data, assisted in creating policies and procedures for reporting and dispute management, and provided
data analysis, guidance and best practices to ensure compliance with regulatory obligations
Michel Berard, Senior Account Executive, Experian
Michael Berard is a Senior Account Executive at Experian Data Quality, a leading provider of data quality and data
management solutions. Michael is focused on helping organizations leverage their data to gain actionable insights,
make faster, data-driven business decisions, and gain a competitive advantage. With more than 7 years experience,
Michael has assisted organizations of all sizes to implement data governance programs, reduce regulatory risk, and
streamline data migration projects.
Rishi Patel, Senior Technical Consultant, Experian
Rishi has over 10 years of experience in data quality software from development and implementation to best practices
and solution strategy. He is an active member in the data quality community as well as building out highly skilled
consultancy practices within Experian focused on Enterprise Applications and architecture. His role now focuses on go
to market strategies and technical subject matter expertise in new and emerging technologies for Experian Data
Quality.

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Agenda
Data accuracy
 Key findings
 Media headlines
 The FCRA data furnisher
rules
Best practices
 Understanding your data
 Regulatory exam surprises
 What other lenders are doing
to prepare
5 steps to boost accuracy
 Key strategies
How Experian can help
 DataArc 360™ powered by
Experian Pandora™
 Results
 Demonstration

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Data accuracy

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90%
of financial institutions believe
increasing regulation has driven their
need for better data analytics and
management
76%
of financial institutions believe
inaccurate data is undermining their
ability to provide an excellent customer
experience
29%
of US companies feel an inadequate
data strategy contributes to a lack of
data accuracy
28% of financial institutions suffer from
regulatory reporting challenges

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Regulatory scrutiny

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Fair Credit Reporting Act (FCRA)
Those that report consumer credit data to the credit bureaus
should comply with data furnisher rules stated in the current
Duty of
furnishers
Obligations under the Fair Credit Reporting Act (FCRA)
Section 623:
Responsibility of furnishers to provide accurate and
complete information to credit reporting agencies1

Experian Public.
Sound policies and procedures can help to validate data quality
and to ensure compliance
Accuracy
guidelines
Obligations under the Fair Credit Reporting Act (FCRA)
Regulation V (part 1022):
Responsibility to create policies and procedures concerning
the accuracy and integrity of furnished information2

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Best practices

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 Providing policies and procedures for Metro 2® data reporting and dispute
handling
 Providing monitoring metrics for reported data
 Provide reporting samples
 Metrics showing the 30-day dispute investigation obligations are being met
 Providing samples of dispute investigation including reviewing additional
documentation provided by the consumer.
Regulatory exam expectations and surprises
EXPECTED

Experian Public.
Regulatory exam expectations and surprises
 Request to provide resumes, organizational charts and qualifications (to see open and vacant
positions)
 Board meeting minutes for any meetings that addressed data furnishing/dispute handling
compliance matters
 Controls on who has access to data including contractors, vendors and servicers (including what
training they are given and procedures)
 Data mapping documents including data ownership and controls by data element, manual reports,
visual basic scripts hot keys, data dictionaries and glossaries.
 Training provided and taken by personnel responsible for data reporting and dispute
 Documentation such as letters, phone calls, emails from and to consumers including recorded calls
from more than a year ago
 If an error was found in sample data, provide a full accounting of all records and prove the accuracy
UNEXPECTED

Experian Public.
How organizations are meeting obligations
Key risk indicators
 Identifying and combining a small set of highly risky statistics to monitor
Benchmarking against peers
 Allows comparison of reporting and dispute policies and brings
perspective to the metrics
Benchmarking internally
 Learning internally and leveraging internal best practices
 Ensure consistent treatment for consumers with multiple products
Validate Metro 2® reported date prior to sending
Building repository to house errors from all bureaus in order
to correct and manage
Large, multi line of business organizations are creating cross-
department oversight teams and using deep metrics
BEST
PRACTICES

Experian Public.
How organizations are meeting obligations
 Disputes and reporting closely aligned
 C-level oversight and responsibility
Organization, internal
awareness and oversight
at the highest levels
 Mapping data from source to Metro 2® output
 Using the insights to formalize governance of the data; ensure
reporting team is a stakeholder in any changes to fields in upstream
systems that produce Metro 2® data
 Enables Metro 2® validation as a part of system regression testing
Data mapping
and governance
 Using dispute metrics to enable root cause analysis
 Testing dispute processes and policies to ensure they address
regulatory requirements
 Digging deep into e-OSCAR data for actionable insights
Deeper insight
into disputes
 Reviewing examples on a periodic basis to ensure accuracy
(system of record, Metro 2® reporting, CRA database and disputes)Sampling

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5 steps to
boost data
accuracy

Experian Public.
Implement data governance
Establish robust data monitoring
Proactively assess data accuracy
Define data quality methodology
Prioritize the consumer
1
2
3
4
5
Five steps to boosting your accuracy in data reporting

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Step 1: define data quality methodology

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 Where does the data come from and how often is it updated?
 Do you have consistent information across all systems?
 How is the information linked or matched together?
Analyze: evaluating data
Data quality, coverage and matching

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 Implement standardization processes
 Transformations of data against business rules
 Review source definitions of phone, address, or email verification
Improve: evaluating quality
Understand standardization and verification

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 Define right party contact at the “account level”
► One or more phones or addresses belong to the consumer
 Reduce wrong party contact—false positive matches
► Phone scoring or address ranking can be used to support custom
contact strategies
 Unique contact data is identified when no other source is available
► How reliable is the unique data?
Control: monitor performance over time
Measuring change or success

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Step 2: proactively assess data accuracy
Assess data accuracy
 Identify and remediate data issues prior to submitting to the bureaus
 Perform data mapping and validation
 Identify root cause of errors and action on findings
 Leverage partnerships (internal/external)
Data accuracy will help to ensure regulatory compliance, operational
efficiencies and drive a positive customer experience

Experian Public.
Step 3: prioritize the consumer
Prioritizing the consumer
 Understand customer experience
 Implement improvements and consistencies in the credit life cycle
 Speedy dispute response and resolution
 Ensure education, awareness and transparency
Accuracy TransparencyConsistency

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Step 4: establish data monitoring
Establish data monitoring
 Analyze reported Metro 2® data and understand industry
benchmarking
 Monitor data reporting rejects
 Receive ongoing data feed of reporting results to easily track
and measure results
Data monitoring will provide a method to identify reporting anomalies, help
to prioritize issues and track trends on an ongoing basis

Experian Public.
Step 5: implementing data governance
Data governance
 Data stewardship and Metro
2® reporting councils
 Business process and
controls
 Consider a single governance
solution that may be
leveraged company wide
 Relationship discovery
Prevents future
data quality issues
Best practices
are shared
Knowledge
is shared
Regulatory
compliance
Benefits

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How Experian
can help

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DataArc 360™: a new data quality solution
Application
Functionality
Results driven
 DataArc 360 leverages a data management tool called Experian Pandora. The
application that is loaded on premise and is accessible via centralized, managed sever
and available securely to named users –application can connect directly to a source
system to upload data
 Metro 2® rules are pre-loaded into Experian Pandora™
 Solution is bureau agnostic and can be used when sending data files to all CRAs
 DataArc 360™ is be pre-coded with 300+ data quality rules: 225 statistics
are checked by 115 compounded Metro 2® rules based on multi attribute dependencies
 Rules may be customized if needed – updates to a rule can be made in less
than 10 minutes
 Identify and remediate data issues prior to submitting to the bureaus
 Analyze / uncover data issues, find root cause, perform ad hoc investigations,
monitor quality over time, and prioritize and justify actions
 Beyond just a visual representation of data, DataArc 360™ can help with data
preparation, perform instantaneous data browsing, filtering and relationship
discovery

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




Pre-built rules
Metro 2®
rules
300 data quality checks
Checks for invalid consumer names / addresses
(special words / phrases)
Checks for potentially embedded data
(SSN embedded within account number)
Over 115 rules and illogical conditions
aligned with Metro 2® reporting
Flags accounts with potentially
inaccurate calculations

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How DataArc 360™ can help
Problem Solution
1
2
3
Regulatory pressure
Meeting FCRA requirements,
undergoing CFPB exams
Data quality
Monitoring for quality, preventing
rejects, remediating reporting issues
Customer experience
Minimizing disputes, reporting
inconsistencies and inaccuracies
Meet regulatory obligations
Validating accuracy of consumer credit data
on the Metro 2® file, proactively preparing
for an exam, implementing policies and
procedures to assess data accuracy
Identify errors on Metro 2® submission prior
to submitting to the bureaus
Analysis of data quality
Drive positive experience
Remediate reporting issues / inaccuracies
prior to sending data to the bureaus, upload
dispute information to monitor and compare
against Metro 2®

Experian Public.
Key findings
Initial data load findings
 Metro 2® rules were deeper in content and higher in
accuracy against SQL attempts
 Database query faster and more efficiently than SQL
developers (roughly 400% faster with deeper results)
 Name failures due to special characters such as periods
and apostrophes

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Key findings
Red flags for data quality issues
 Account number check failed due to account numbers
with embedded blanks
 Borrower is less than 18 years of age
 Authorized user reported with missing date of birth
 Date of first delinquency is too recent for account status
reported

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Demonstration

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DataArc 360™ screen shots

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Dashboard
Error name
and score

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For more information please visit our website at:
https://www.edq.com/products/pandora/dataarc-360/

Experian Public.

Boost your data accuracy with these 5 steps

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