Recently presented at a Data Management/Risk and Regulatory compliance conference at NYC to talk about data aggregation and collation challenges. Spoke on common Data Management challenges faced by financial institutes with Risk & Regulatory compliance and shared key learning's from my experience that could help accelerate data programs, reduce cost and delivery timelines.

1. CECL
Data Management Challenges & StrategyData Management Challenges & Strategy

2. Bio
Ashish Mahajan
Ashish is a Senior Data Management Executive with 20+ years of Experience in
Finance/Banking, Automotive and IT consulting industries. He has extensive experience in defining and
executing Data Strategies and leading Data driven Business Innovation & Change. Ashish has led the
build and delivery of Data Lakes, Data Warehouses and stores for Risk & Regulatory compliance at large
Banks.
 Experience
– Head of Data Management at TD Bank, USA
• Risk and Regulatory Compliance (CCAR, Liquidity, Credit Risk, Dodd Frank, IFRS 9.0, ..)
• Data Strategy and Enablement
• Data Reporting and Analytics• Data Reporting and Analytics
• Data Quality, Reference Data, Metadata, Masterdata Management
– Executive Director, Enterprise Information Management at PNC Bank
• Data Management, Risk and Regulatory Compliance (CCAR, Liquidity, RWA..)
• Steering data innovation via new technologies – Big Data, Sandboxes, etc.
– General Motors
– Deloitte Consulting
Contact: ashishmahajan02@gmail.com

3.  Data Management Technologies now enable storage and
processing of larger volumes of data than ever before
 Expect Business to demand more data and at a faster pace
 Regulatory changes will demand data retention, processing
ACriticalEnabler
Regulatory changes will demand data retention, processing
agility and management of larger volumes of data including
historical data
Data–ACriticalEnabler
"CECL requires significant changes to the data a bank maintains and analyzes. Bankers, regulators, and auditors are in agreement that more granular data
and analysis will be required and new performance metrics will be needed."
Reference: ABA, June 2016

4. Data
Requirements for
CECL
Data Mapping &
Gap Analysis
Data Operations
(re-
New
Regulatory
mandate -
CECL
DataLifecycleforRegulatoryCompliance
CECL Data
Support
Data Life-Cycle
Liquidity
Call ReportingCall Reporting
FRY 14
Data Exploration
& Remediation
CECL Data
Integration &
Quality
Data Extraction &
Consumption
(re-
use, archival, etc.)
DataLifecycleforRegulatoryCompliance
SCCLSCCL
STRESSMODELS
BASEL
BCBS 239
CCAR
Data Lifecycle for
Risk and
Regulatory
Mandates

5. Questions to ask Recommended Resolution/Steps
What data is required/captured/available?
 Data mapping & exploration
 Leverage existing Risk and Reg. Data Stores
Is GAP data available at source?
 Data mapping and analysis to origination systems
 Identify data gaps (if any) at loan origination
Is Historical Data Required? How many years?
 Historical data availability within existing Risk & Reg.
data stores
 Core and Warehouse archives
 Initiate data retention
What is the quality of data?
 Identify rules to ensure availability, timeliness, accuracy,
integrity of data
DataManagementChallenges
What is the quality of data?
integrity of data
What Reference and Meta Data is required?  Identify Reference and Metadata
CECL Specific Data Impacts – Expected loss over incurred
loss, Historical data for Life of loan/Portfolio.
 Identify attributes currently not being captured
 Historical loan loss data
Data capture at loan origination  Initiate core system changes
Risk&RegulatoryPrograms-DataManagementChallenges
historical data sets
maturitydates
term
Facility Ratings
Borrower Risk Ratings
GapData
Reference Data
sox
economic cyclesSCCLSCCL
CCAR
BASEL
STRESSMODELS
IFRSIFRS
BCBS 239
CECL

6. DataDiscovery,Exploration&Mapping
CECL data
Finance
Accounting
Risk
Model
Audit
Retail
Commercial
Securities
Loan id
Days past
Interest rates
instrument
gross amt.
Payment fr.
Int. rate chg
Loan amt.
term
Maturity dt.
Orig. dt
Adjustments
FICO
BRR
FRR
Orig. risk rtng.
Payoff dt.
portfolio
Product typ
segment
city
state
collateral
Facility id
Loan currency
Customer
name
LTV
Facility amt
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existing data new CECL
attributes
Macro
Economics
DataDiscovery,Exploration&Mapping
CECL dataModel
Technology
Commercial
Retail
securities
Securities
Other
Chargeoff amt
Charge-offs
Recoveries
Illustrative
Reference: Basel, CCAR, Living Wills, LCR, IFRS 9 data preparation
Chargeoff dt.
zip
country
Recovery amt.
Recovery dt.
Third party
data
NAICS
Moodys
D&B
Market data
Mark to
market
Mark to
market
Lien pos
Gurantee
SIC
Write off
Company typ
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7. DataCollection&GapAnalysis
Data Gap Analysis
Data Mapped &
Available
Unclear
Requirements
Gap
Under Analysis
Loan id
Days past
Interest rates
instrument
gross amt.
Payment fr.
Int. rate chg
Loan amt.
term
Maturity dt.
Orig. dt
Adjustments
FICO
BRR
FRR
Orig. risk rtng.
Payoff dt.
portfolio
Product typ
segment
city
state
collateral
Facility id
Loan currency
Customer
name
LTV
Facility amt
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new attr.
DataCollection&GapAnalysis
Under Analysis
Chargeoff amt
Chargeoff dt.
zip
country
Recovery amt.
Recovery dt.
Third party
data
NAICS
Moodys
D&B
Market data
Mark to
market
Mark to
market
Lien pos
Gurantee
SIC
Write off
Company typ
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8. DataCollationChallenges
• Loan origination & Servicing Data
– Life of Loan, expected vs. incurred
– Appropriate Loss expectation data attributes are
already captured (e.g. Appraisal, LTV, Collateral
assessment, etc.) or will need to be captured (net new
data)
– May require data recovery from loan application files
for interim
• Acquired Portfolio's
– Data Gaps
• Granularity of data
• Quality of Existing Data
– Validity of data – Maturity Dates, Term, etc.
engage data & technology organizations early
initiate core system data capture changes
influence process changes now e.g. historical
data capture from core systems
what should you be doing and what to expect from your data organizations
Identify data controls, audit and other
compliance controls (SOX, etc.)
Initiate retaining loan history in data
warehouses
CECL–DataCollation
– Validity of data – Maturity Dates, Term, etc.
• Historical Data Sets
– Charge off data
– Economic cycles, validity of data, justification
– Transactions
• Data Audit
– Quality of Data
data sampling and quality checks
CECL critical data element list
Data integration needs e.g.
underwriting, origination, chargeoffs, etc.
Define data strategy for sourcing, integration &
consumption

9. Risk&RegulatoryDataManagementFramework Data Management
Reg. & Risk Compliance
(CCAR, LCR, RWA, Living Wills, FRY-
xx, Impairment, CECL, etc.)
Governance
Communication
Data Consumption
(Reporting, Risk Models, etc.)
Reconciliation & Adjustments
Internal Supervision
External Supervision
Risk&RegulatoryDataManagementFramework
Data Architecture & Infrastructure
People/Talent
Data
(Risk & Reg. Warehouses, Big Data, Marts)
Data Management Capabilities
(Data Quality, Metadata, Reference Data, Hierarchy, Ingest, Integration, Consumption, etc.)
CECL, (Data) Guiding Principles
 Re-use vs Create
 Leverage existing Risk and Regulatory data stores & infrastructure
 Expect changes and refinement to rules
 data management processes and technology should scale
 Agility
 Capture Width and Depth of data
 Grain
 History

10. DataManagementLifecycle
Servicing
Origination
Recoveries
Collections
Deposits
Securities
External Data
ALLL
CCAR
Living Wills
Risk & Regulatory Data Management
Stage
IntegratedData
RWA
Basel
SPOT
Capital
IFRS 9
Raw Data
Data Quality, MetaData, Reference Data
Consumption
ModelManagement
Risk&RegulatoryApplications
1
3
DataManagementLifecycle
Manual Data
Historical Data
Big Data
Raw Data (Historical Data)
Data Exploration Modeling
Liquidity
SCCL
Risk&RegulatoryApplications
Archives
Document
Management
CECL
2
CECL Data Required =
1 2 3 4 5
54
What data is currently being captured?
What & How much historical data is currently available?
What net new data is required?
Can archived stores provide missing historical data?
Can content management systems be source to missing data?

11. GetLiftfromExistingRisk&RegulatoryStores Technology Business
Data
Warehouse
Credit Risk
Assess Data Availability in Existing Data Stores
 Engage Your Data Management Organization early
 Articulate Data Requirements
 Analyze sample data sets
 Avoid Data Management having to play catch up
Existing
Risk & Reg. data stores
- loan data availability
- collateral & other data sets
- Risk ratings
- Other data sets
1
data gap analysis - ?
- is gap data available upstream
- are there data gaps upstream with origination and servicing systems
- can content management systems provide gap data
Data Gap Analysis
 Data Available versus Expected, Critical Data Elements
 Quantify gaps with existing capture process
 Identify gaps in origination and servicing systems
 Initiate data remediation efforts to core systems early
2
Data quality checks
 profiling of critical data elements Assess Quality of Available data
Historical Data Requirements for CECL Model
 How much history by asset class/portfolio
 Transaction vs. month end
 Must have vs. nice to have
 Acquired portfolio's
CECL–GetLiftfromExistingRisk&RegulatoryStores
 Tremendous success with IFRS 9
 Cost & Time Efficiencies
 profiling of critical data elements
 Identify DQ rules
 Aggregation and transformation logic
Assess Quality of Available data
 Consistency, completeness, accuracy3
Historical data requirements
- do existing warehouse capture history &how much is available
- archived stores
- If not storing history, start capturing raw historical data
4