Check out this SlideShare to understand the challenges of BCBS 239 and learn ways to collect, measure, monitor and report on data to achieve better data integrity and data quality. Both G-SIBs and D-SIBS will learn how to help better govern their data.

1. BCBS 239 – Challenges & Approach

2. Supervisory ReviewsRisk ReportingRisk Aggregation
Bank Challenges in Addressing BCBS 239
2
BCBS 239 Pillars
Data ArchitectureData Governance
Business Challenges
BCBS is not a cook book –Best practices round up to be followed from Ground up
Past M&A created overly complex and rigid IT landscape
Need to find consensus on data issues like DQ, data definitions, data availability,
accountability, storage and retrieval process
Complexity, size and availability of data used by banks lead to a lack of data adaptability
- size is a paradoxical factor
Aggregating data on a cross-border level and reconciliation of the data at legal entity level
is more complex
Risk reports that are produced within IT landscapes are generally standardized, database
specific reports with predefined frequencies and parameters
Banks find it difficult to determine how authorities are going to assess their compliance
Data IntegrityData Quality Metadata
Management
Data Integration
Data Reconciliation Data Aggregations
Multi Level
Reporting
Information
Architecture
Information
Governance

3. Data Quality Data Integrity Architecture Governance Aggregation Reporting Integration Reconciliation Metadata
G-SIB’s
In place- not
complete
Minimal–but
more manual
Progressive Progressive In place –
Complex
In Place –
Complex
In place –
Mature
In place –
Selective
Progressive
D-SIB’s
In place-not
complete
Minimal–but
more manual
Evolving Evolving In place –
Complex
In Place –
Complex
In place –
Mature
In place –
Selective
Progressive
Banks Maturity in US
3

4. Challenges in D-SIB’s for Governance Team
4
We have it – but we are not sure how much more we should
invest in DQ and where?
We thought about it – We haven’t done because of time to
deliver when we built out ETL
Yes – it is complex, We need more architects – but
re-architecting is a great idea ?
Data Quality
Data Integrity
Architecture
In place-not complete
Minimal – but more manual
Evolving

5. Challenges in D-SIB’s for Governance Team
5
We have our policy in place – We need to be more operational.
Need more metrics/ inventory – I would like to know what went in
daily and who is using what?
Its complex, done at multiple source levels – We have
documented it – but not sure how it works end-to-end?
Sure we have more reports than our Eemployees count! – but
it’s a challenge to make my business trust my reporting data
and do fact based decisions
Governance
Aggregation
Reporting
Evolving
In Place – Complex
In Place – Complex

6. Challenges in D-SIB’s for Governance Team
6
We have a great ETL Team and tools – we also have too many
jobs to manage and monitor, our support is complex
Sure we do it for our financial books – but not across all the
process gates for the risk data and reporting?
Metadata tool is in place – We have business glossary, but it’s a
challenge to keep up with the changes and metadata in sync
Integration
Reconciliation
Metadata
In Place - Mature
In Place – Selective
Progressive

7. BCBS 239 – Next Steps?

8. BCBS 239 – Collect, Measure, Monitor and Report
Collect – Build Inventory of key risk data used in data aggregation and reporting
• Risk data elements, risk parameters, counterparty information, risk model parameters – needs
to be identified both in standardized form and how they are represented in the origination
system
• Group data elements to KDE, DDE, SDE and MDE’s to assignee right DQ gradient to
measure the data points usefulness
• Validate what governance policy and process influence the KDE & DDE data points and
capture/report audit points to manage the policy adherence
• Report data inventories corresponding to KDE/DDE/SDE origination source to ensure
frequency and timeliness parameters are met
• Connect – Key data attributes with metadata and DQ initiatives to bring in transparency
• Build application level data inventory and connect application level inventory to produce
enterprise level data inventories
• Refresh your data inventory through automation – to keep information metadata more current
• Assignee owners/custodians of the data, provide them with the visibility reports on data
metrics
• If possible, attach data metrics around each fields with your metadata tools – to bring in data
related trust on the attributes
8

9. BCBS 239 – Collect, Measure, Monitor and Report
Origination Source Data Management Apps Data Aggregation Rule Reporting Systems Audits/External Extracts
Control Metadata
Data Metrics Measured and Captured EndEnd
Metadata
Reporting &
Dashboards
• Capture metrics such as Counts, Volumes,
Date Ranges for a given data set – and
compare it with your organization
benchmarks/SLA etc. – this can be your
early warning system!
• Look at source data patterns, trends –
before and after aggregation rules –
Compare them with benchmark expected
results – to ensure your aggregation is
working as expected
• Reconcile your data sets with the golden
source of the data – build as many
reconciliation steps as possible in your flow
– this can help improve data trust with
business teams
• Identify specific data Quality parameters for each critical data
elements and always look for deviations from normal
• Build a consolidate Application Level Data trust score, make
Application owners responsible for managing the scores.
9
Measure your data that is flowing in from different applications and used by
reporting teams – If you can not quantify, you can not govern well

10. BCBS 239 – Collect, Measure, Monitor and Report
• Leverage analytics to predict out of
boundary data issues and resolve data
issues
• Review Data Quality Scores daily – check
if the score change warrants an
investigation
• Benchmark Industry data points if any – to
validate any adverse changes in key data
elements
• Monitor recon difference at each points in
the data movement process
• Share monitoring report to business teams
if there are data issues above threshold
10
Monitor the data flows daily to catch any data anomalies or abreactions in
consolidated data metrics.

11. BCBS 239 – Collect, Measure, Monitor and Report
• Provide end-to-end trend reporting dashboards – to
business users each time they would like to make
decision using the data – this can help build data
confidence
• Compare data issues from support group with the trend
deviations to make insight into data issue root cause
• Look at reconciliation deviations by trends – understand
the different patterns to improve accuracy parameters
• Share reports to source application teams – explore
DQ initiatives that can be focused and provide high
value in ROI
• Explore option of using analytics to predict data issues
before they come – plan to mitigate data issues before
$175
$200
$250
$300
$350
$100
$125
$170
$200
$275
$300
$350
$400
$450
$525
$275
$250
$350
$325
$375
$- $200 $400 $600 $800 $1,000 $1,200 $1,400 $1,600 $1,800
2011
2012
2013
2014
2015
Exposures in $ Mn
Years
Y0Y Retail Exposure Growth
-50
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Daily Diff GL Balance Src Balance
11
Report Data trends on your risk KDE data

12. BCBS 239 – How can we help? – Tool Demo

13. Infogix – Control & Reporting
13
We can provide controls at each points – to measure, monitor and promote  Data Integrity, DQ
improvements, Data Trust, Data Transparency and help you govern your data better.
Origination Source Data Management Apps Data Aggregation Rule Reporting Systems Audits/External Extracts
Control Metadata
Data Metrics Measured and Captured EndEnd
Metadata Reporting & Dashboards

14. Infogix Demo – Loan Origination Example
1. Consider a Loan Origination System which has
multiple source of originations like e-comers, 3rd
party, MF systems
2. Its common to see your Loan Application Apps (3rd
party application) that are used to track/approve
loan request
3. Approved loans are posted in the Loan Processing
Systems – and data feed to EDW systems daily
4. Loan Processing System post loan data to
Financial GL systems, as well sends daily feeds to
EDW
5. Internal retail data marts used for loan reporting at
departmental level gets data feed from loan
systems and/or EDW
6. Risk data warehouse – gathers risk data points
from the EDW, loan systems, Financial GL – for
internal or external reporting
Challenges:
1. Lack of data level visibility between origination, application,
processing, financial books and reporting
2. Possibility of data integrity questions arises each time we have a
data issue – creating data trust challenges!
3. Discrepancies in loan reporting at GL level, departmental
reports and Enterprise Risk Reports
4. Entity Level Risk Roll ups – might not match at department level
risk exposures
5. Complex data transformations, ETL, aggregations – can some
time break information value – data quality with right parameter
is a challenge.
14

15. Loan Origination Example
15
Might need reconciliation at different level to ensure we report the same across the enterprise.
3rd party Loans
E-Loans
Loan Origination
Loan Approval
Systems
Unapproved Loans
Loan Processing
Systems Financial
GL System
P&L Reporting
Loan
Originations
Information
Management Loan reportingLoan Recon
Retail Reporting
Mart
Risk Warehouse
Enterprise
Warehouse
Internal Risk Reporting
Retail Dep. reporting
External FFIEC
Reporting
# Input
$ Input
# Input
$ Input # Exclude
$ Exclude
# Output
$ Output
# Loans
$ Loans
# Output
$ Output
# Output
$ Output
# Total Input
$ Total Input
# Input
$ Input
# Output
$ Output
# Excluded
$ Excluded
# Input
$ Input
# Input
$ Input
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Input
$ Input

16. Loan Origination Example
16
3rd party Loans
E-Loans
Loan Origination
Loan Approval
Systems
Unapproved Loans
Loan Processing
Systems Financial
GL System
P&L Reporting
Loan
Originations
Information
Management Loan reporting
High Level Loan Origination System with multiple data touch points – might lack data visibility.
Loan Recon
Retail Reporting
Mart
Risk Warehouse
Enterprise
Warehouse
Internal Risk Reporting
Retail Dep. reporting
External FFIEC
Reporting
# Input
$ Input
# Input
$ Input # Exclude
$ Exclude
# Output
$ Output
# Loans
$ Loans
# Output
$ Output
# Output
$ Output
# Total Input
$ Total Input
# Input
$ Input
# Output
$ Output
# Excluded
$ Excluded
# Input
$ Input
# Input
$ Input
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Input
$ Input

17. Loan Origination Example
17
Data Quality needs to be measured multiple touch points to ensure KDE are optimal.
3rd party Loans
E-Loans
Loan Origination
Loan Approval
Systems
Unapproved Loans
Loan Processing
Systems Financial
GL System
P&L Reporting
Loan
Originations
Information
Management Loan reportingLoan Recon
Retail Reporting
Mart
Risk Warehouse
Enterprise
Warehouse
Internal Risk Reporting
Retail Dep. reporting
External FFIEC
Reporting
# Input
$ Input
# Input
$ Input # Exclude
$ Exclude
# Output
$ Output
# Loans
$ Loans
# Output
$ Output
# Output
$ Output
# Total Input
$ Total Input
# Input
$ Input
# Output
$ Output
# Excluded
$ Excluded
# Input
$ Input
# Input
$ Input
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Input
$ Input

18. Loan Origination Example
18
Complex Aggregation Rules needs to be validate at each level.
3rd party Loans
E-Loans
Loan Origination
Loan Approval
Systems
Unapproved Loans
Loan Processing
Systems Financial
GL System
P&L Reporting
Loan
Originations
Information
Management Loan reportingLoan Recon
Retail Reporting
Mart
Risk Warehouse
Enterprise
Warehouse
Internal Risk Reporting
Retail Dep. reporting
External FFIEC
Reporting
# Input
$ Input
# Input
$ Input # Exclude
$ Exclude
# Output
$ Output
# Loans
$ Loans
# Output
$ Output
# Output
$ Output
# Total Input
$ Total Input
# Input
$ Input
# Output
$ Output
# Excluded
$ Excluded
# Input
$ Input
# Input
$ Input
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Input
$ Input

19. Loan Origination Example
19
Infogix Controls – For Measuring, Monitoring and Quantify Data across the touch points.
3rd party Loans
E-Loans
Loan Origination
Loan Approval
Systems
Unapproved Loans
Loan Processing
Systems Financial
GL System
P&L Reporting
Loan
Originations
Information
Management Loan reportingLoan Recon
Retail Reporting
Mart
Risk Warehouse
Enterprise
Warehouse
Internal Risk Reporting
Retail Dep. reporting
External FFIEC
Reporting
# Input
$ Input
# Input
$ Input # Exclude
$ Exclude
# Output
$ Output
# Loans
$ Loans
# Output
$ Output
# Output
$ Output
# Total Input
$ Total Input
# Input
$ Input
# Output
$ Output
# Excluded
$ Excluded
# Input
$ Input
# Input
$ Input
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Output
$ Output
# Input
$ Input
Control Metadata
Metric Reporting
Mail Alerts/Warnings

20. BCBS 239 – Infogix Control Dashboards

21. Infogix – Control & Reporting
 Provide KPI Reporting by data groups
across DQ gradients
 Ability to compare key elements
across gradients
 Identifying trends and data occurrence
by each elements
21

22. 200+Customers
INCLUDING:
20
of the
Fortune
100
7
of the top 10
Commercial
Banks
6
of the top 10
P & C
Insurers
of the top 5
Health
Insurers
4
Million
wireless cable
& broadband
subscribers covered
400
30+years as a leader in
analyzing data across
the enterprise.
500+Employees (and growing)
revenue represented
by our customers.
›$1 Trillion
›96%ANNUAL
CUSTOMER
SATISFACTION
RATING
1Million+ total Infogix business rules
running for our customers.
72%
Managing Risk is #1
of customers report it as the
key benefit of utilizing Infogix.
›15
YEARS
average number of
years that customers have
partnered with Infogix.
22

23. Thank You
Visit www.infogix.com for more information
Or contact kparal@infogix.com for more
information
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