1. Senior Information Systems Specialist
Business Case
for Formulas
Jon Wisnieski
Senior Information Systems Specialist
Hi, my name is Jon Wisnieski and I am a Senior Information System Specialist at the Federal Deposit Insurance Corporation. In 1975 I began my career with FDIC and for the past 28 years I have focused my career on data collection, validation, and publications. During the next 10 minutes I would like to share some of my experiences with you.

2. Agenda XBRL Application Quality Standards and
Formula Benefits
Quality Standards and
Business and Performance Metrics
Today I have four areas that I would like to talk about
The XBRL application and how it fits within the Central Data Repository (CDR) model
The Benefits of using a standard formula language like XBRL
Not only to describe your data quality standards
Talk about the FDIC’s next steps in publishing and generating business and performance metrics, including the creation of comparable peer statistics

3. XBRL Application
Three banking agencies developed the Central Data Repository (CDR)
Used XBRL to define and transport data
Data receipt
Data validation
Storage
Distribution
CDR launched on October 1, 2005
Key policy change ~ pre-validation using XBRL
Very Successful implementation
In 2001 the FDIC, FRB and OCC developed the CDR project…we agreed to work together and created a MOU that describes how we manage the project, including resolution of differences, cost and budgetary issues and the day-to-day management of the project.
USED—XBRL was used to communicate meta data requirements to all users and to provide a standard for transporting data
RECEIPT—When data are received into the CDR, the system retains a copy of the XBRL instance document and data are loaded into a DBMS.
This process maintains a record for all transactions for legal purposes
VALIDATION—CDR uses the same requirements that is sent out in the taxonomy and will not accept data that does not meet the FDIC Validation requirements, as specified in the taxonomy
All Math edits must be met without exception
If a comparison edit does not meet the meta data validation requirement, the respondent must supply an acceptable explanation
STORAGE—system is an internet application, managed by Unisys
DISTRIBUTION—agencies extract data from the CDR into their respective legacy systems
KEY—policy change—CDR will not accept data from respondents unless the data meets pre-established meta data requirements… respondents are required to manage their data

4. Call Reporting Before XBRL
Validation routines and formulas stored in and processed by two systems (FRB, FDIC)
Banks submit data after some minimal checks in their software - inconsistencies between preparation software packages
Software vendors receive Call Report metadata from Excel, PDF, and Word documents – cut and paste into their software
Agency analysts would check data quality once files had been submitted and contact bankers with any questions – often 1-3 weeks after initial submission
This process worked, but there was a significant amount of user interpretation of requirements.
In other words, we had difficulties in communicating with each other: banks, vendors and agencies

5. Call Reporting After XBRL
FFIEC developed the XBRL-based CDR with Unisys Corporation as systems integrator
Metadata stored in XBRL taxonomy files now available to anyone
The same taxonomy files that contain validation criteria the agencies use in the CDR are used in Call Report software vendor packages
Banks are required to check the quality of their data before submitting
Agencies do not accept data with quality problems
Quality assurance work is done by reporters up front, when it is more efficient
Agencies receive high quality data sooner—lower cost
Post implementation observations:
Respondents did not know that they were using XBRL
Vendor’s interfaces masked technical changes e.g. using web services, accessing the prior period CDR data
Since this was an interactive web based process, all parties could monitor when changes occurred

6. Benefits XBRL is Expressive … and therefore powerful
A standard for expressing:
the data to be exchanged
the instructions for providing the data
an interface or form or presentation
the validation criteria for checking the quality of the data
By using a taxonomy or set of definitions and rules Everyone Sees the Same Data!
Taxonomy = authoritative source, used by all
Rules that describe how to report your data…
What if you are expecting a debit balance--what error is raised if it has a credit balance?
What if a data concept should only be reported at year-end--what error is raised if it is reported at other times?
What if a series of concepts should balance to zero—and they do not?
What if the sum of accounts should equal the balance brought in from another control file—and the do not?
What if you are expecting a response of 1, 2, or 3—and its is a 5?
What if a concept was reported last submission--but is not populated this submission?
What if a variance for a concept is greater than your expectation—what do you do with the data?
Relationships between data
Total assets on the balance sheet must equal total assets in a different section
Presentation
Traditional balance sheet, income statement, cash flow -- versus
Hierarchal presentation—a series of parent child relationships
Do not need to choose one over the other you can have both or more!
Report Instructions
Data quality standards
Use of formulas
Communication between all parties improved—within the FDIC’s CDR project
Banking agencies
Call Report Software Vendors
Financial institutions
Increased Data Transparency

7. Quality Standards What are they?
Formulas that are expressed in XBRL and shared with stakeholders
Evaluate to either ‘true’ or ‘false’
Check a relationship that either must be true
– or –
that, if true, point to an anomaly to be researched
You may ask how this is achieved?
As I mentioned it is because we are all on the same page via the taxonomy.
In the taxonomy, we have a “formula linkbase”. This idea is discussed in a white paper that has been drafted, and a copy of it can be found on the FDIC Web site.
The formula linkbase contains all the data validation criteria for the Call Reports.
Two types of formulas
Validity formulas check for relationships that must be true (assets = liabilities + owners equity)
Quality formulas check for relationships that may point to data anomalies (assets grew by 100%)
The use of the XBRL taxonomies and the linkbase enabled us to transform our business process by requiring pre-validation of the data before the bank reports the data.
We plan to extend the use of this concept for use with the Basel II and other data collected via CDR.

8. Quality Standards
Validity – equations that must hold true or the data is inaccurate
Quality – data relationships that help identify anomalies
Reportability – identify what financial concepts an entity should submit based on their structural or financial characteristics

9. Business and Performance Metrics
What are they?
Modify data by (+, -, /, *)
Apply functions (annualize, %change)
Consistently applied across
Data
Industry
Comparability

10. Business and Performance Metrics
Capital Adequacy
Asset Quality
Earnings
Liquidity
Growth Rates
Industry Standards
Regulatory
International ~ Basel II

11. Results—Everyone Sees the Same Data!
Business and Performance Metrics
Taxonomy = authoritative source, used by all
Rules for what data to report
Data quality standards
Communication between all parties improved
Banking agencies
Call Report Software Vendors
Financial institutions
Increased Data Transparency
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12. Questions - Comments?