Real-time risk management coupled with the requirements for regulatory reporting are top of mind for many heads of risk; to meet the demands of new regulation financial organizations must have technology that enables the business to easily calculate and analyze risk across products and channels. In this webinar, we will cover how organizations use MongoDB for: * Implementing proactive risk controls * Aggregated Risk on Demand, Creating an Adaptive Regulatory Reporting Platform * Cost Effective Risk Calculations

1. How Financial Services
Organizations use MongoDB for
Real-Time Risk and Regulatory
Reporting
- Jim Duffy: Business Architect Global Financial Services
- Kunal Taneja: Solutions Architect Financial Services

2. 2
Agenda
• The Challenges
• Evolution of Information Management in Finance
• Common Positioning of MongoDB for Risk & Regulatory
• 4 Important terms
• How MongoDB’s cluster topology addresses Risk &
Regulatory Challenges
• Aggregated Risk on-Demand

3. 3
Challenges
The single largest Challenge
in Risk Management is
Achieving a Holistic and up to
date view of the Business

4. 4
Challenges in Regulatory Requirements
2012 2013 2014 2015 2016 2017 2018 2019
ICB Ring-fencing
ICB Loss
Absorbency
Leverage
Ratio -
Basel III
NSFR –
Basel III
MiFID II
T2S
LCR –
Basel III
ICB /
Competition
Audit
Policy
Cross
Border Debt
Recovery
Financial
Transaction
Tax
Market
Abuse
Directive
(MAD II)
PRIP
Accounting
Directive
Review
AIFM
Directive
EU
Transparency
Directive
EU Reg on
Credit
Rating
Agencies
CRDV
Internal
Governance
GuidelinesFATCA
PD
EMIR
SWAPS Push
Out – Dodd
Frank
Securities
Law
Directive
(SLD)
Volker Rule –
Dodd Frank
Short
Selling
Close Out
Netting
Crisis
Management
Recovery &
Resolution

5. The Evolution of Information
Management in Finance
- Jim Duffy Business Architect Global Financial Services

6. 6
Evolution of Information Management
Risk
Compute
Grid (VaR)
Regulatory
Reporting
Platform
Market Abuse
and Compliance
Markets, MTFs,
Internal Liquidity,
etc
EquitiesSwaps DerivativesRates

7. 7
Asset Class Silos
Warehouse /
Repository(s)
Risk
Compute
Grid (VaR)
Regulatory
Reporting
Platform
Market Abuse
and Compliance
Reference
Data
Markets, MTFs,
Internal Liquidity,
etc
Operational
Systems
Operational Data
Store(s)
Reporting
Operational
Systems
Reporting
Operational
Systems
Reporting
Operational
Systems
Reporting
Warehouse /
Repository(s)
Operational Data
Store(s)
Warehouse /
Repository(s)
Operational Data
Store(s)
Warehouse /
Repository(s)
Operational Data
Store(s)
EquitiesSwaps DerivativesRates

8. 8
Cross Asset Class Warehouse / Repository(s)
Risk
Compute
Grid (VaR)
Regulatory
Reporting
Platform
Market Abuse
and Compliance
Reference
Data
Markets, MTFs,
Internal Liquidity,
etc
Operational
Systems
Operational Data
Store(s)
Reporting
Operational
Systems
Operational
Systems
Operational
Systems
Operational Data
Store(s)
Operational Data
Store(s)
Operational Data
Store(s)
EquitiesSwaps DerivativesRates
Cross Asset Class Data Warehouse

9. 9
In Memory Cache, Replication and Relational Database Technology
Markets, MTFs,
Internal Liquidity,
etc
Operational
Systems
Operational Data
Store(s)
Data Services / Reporting
Operational
Systems
Operational
Systems
Operational
Systems
Operational Data
Store(s)
Operational Data Store(s)
Risk
Compute
Grid (VaR)
Regulatory
Reporting
Platform
Market Abuse
and Compliance
Reference
Data
Cross Asset
Data
Warehouse
EquitiesSwaps DerivativesRates
Cross Asset Class Caching Layer

10. 10
Markets, MTFs,
Internal Liquidity,
etc
Operational
Systems
Data Services / Reporting
Operational
Systems
Operational
Systems
Operational
Systems
Operational Data Layer (ODL)
Risk
Compute
Grid (VaR)
Regulatory
Reporting
Platform
Market Abuse
and Compliance
Reference
Data
Cross Asset
Data
Warehouse
EquitiesSwaps DerivativesRates
mongoDB as an Operational Data Layer

11. What is an ODL?

12. 12
4 Important Terms
• Shard: Essentially a partition of horizontally scaling data
• Replica: Copies of data for high availability, disaster
recovery and work load isolation
• Shard Tagging: Method of dispatching data in a cluster
• Replica Tagging: Method of isolating work loads in a
cluster

13. 13
mongoDB Terminology
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary

14. 14
mongoDB Terminology
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary

15. 15
mongoDB Terminology
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary
Shard
Shard: A subset of a horizontally scaling data set

16. 16
mongoDB Terminology
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary
Shard Replica
Shard: A subset of a horizontally scaling data set
Replica: A copy of a data set for high availability,
redundancy and work load isolation

17. 17
mongoDB Terminology
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary
Shard Tagging: Dispatches writes by asset class and geography
Shard Tag By Asset Class and Geography

18. 18
mongoDB Terminology
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary
Replica Tag dedicated to the Intraday VaR data service
Shard Tagging: Dispatches writes by asset class and geography
Replica Tagging: Ensures isolation of work loads
Shard Tag By Asset Class and Geography

19. mongoDB in the context of Risk

20. 20
Active Risk Control Framework
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary
Task: Implement globally consistent active risk controls while
maintaining local governance of asset class specific controls

21. 21
Active Risk Control Framework
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary
Task: Implement globally consistent active risk controls while
maintaining local governance of asset class specific controls
Blacklisted instruments centrally controlled and monitored

22. 22
Active Risk Control Framework
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary
Task: Implement globally consistent active risk controls while
maintaining local governance of asset class specific controls
Blacklisted instruments centrally controlled and monitored
Asset Class specific controls locally governed

23. 23
Adaptive Regulatory Reporting
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary
Task: Implement a cross asset class regulatory reporting platform
which will keep pace with change and enable a 360 degree view of risk

24. 24
Adaptive Regulatory Reporting
EquitiesSwaps DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary
Task: Implement a cross asset class regulatory reporting platform
which will keep pace with change and enable a 360 degree view of risk
MiFID2Dodd-Frank
NFA, CFTC, FSA, etc
MiFID2

25. 25
Benefits of an Operational Data Layer
• Change management of source systems is handled
by the dynamic schema
• Elimination of many data stores for one data layer
cuts down cross-talk and data duplication
• Having one data layer geographically distributed
allows global governance and a holistic view while
not impeding local entities to function as need be
• Workload isolation is achieved via tagging data for
specific use

26. Aggregated Risk on Demand
- Kunal Taneja Solution Architect Financial Services

27. 27
• Regulators are pushing for “better” Risk
aggregation capabilities in banking post 2007
Aggregated Risk on Demand
http://www.bis.org/publ/bcbs239.pdf

28. 28
Aggregated Risk on Demand
Principle 4 – Completeness
• “… Data should be available by business line, legal entity, asset type,
industry, region and other groupings that permit identifying and
reporting risk exposures, concentrations and emerging risks”
EquitiesBonds DerivativesRates
US EU Asia US EU Asia US EU Asia US EU Asia
Primary
Secondary
Secondary

29. 29
Aggregated Risk on Demand
Principle 5 – Timeliness
• “…A bank should be able to generate aggregate and up to date risk
data in a timely manner while also meeting the principles relating to
accuracy and integrity, completeness and adaptability ….”
Cross Asset
Data
Warehouse
Operational
Systems
Operational
Systems
Operational
Systems
Operational
Systems
EquitiesBonds DerivativesRates
Extract – Transform - Load
VaR
Calculator
Time??

30. 30
• Historical Simulation
– Recent surveys points to gaining acceptance of this methodology
– Basic versions of this methodology don’t make use of Var/CoVar
• Generate future scenarios by making use of historical market data
– 1 day holding period using 220 days of history
– 10 day holiday period using 2200 days etc..
• Re-value position based on simulated return scenarios, order the loss
distribution and read of and confidence level (99% VaR or 95% Var)
Aggregated Risk on Demand
Historical Simulation

31. 31
• Fast access to large amounts of stored data
– Historical data spanning up to 10 years
• Parallel aggregation across stored data
– Sort time series
• Scale out and Parallel execution across stored
data
– Use Map Reduce e.g. Black-Scholes
• Flexible schema (document) for storing return
series
– Linear scalability and de-normalise without Joins
Aggregated Risk on Demand
Why MongoDB?

32. 32
Aggregated Risk on Demand
Why MongoDB?
Primary
Risk Application
(Historical Simulation)
Aggregation
Aggregation Aggregation
Operational
Systems
Operational
Systems
Operational
Systems
Operational
Systems
EquitiesBonds DerivativesRates
Aggregation
Quant Library
Aggregation
Aggreg
ation

33. 33
“Book_1”
“Book_1_eq”
“Book_1_eq_ftse”
“Book_1_ir” “Book_1_fx”
“Udf_h1”
An approach with Monte Carlo Sim
Representing Hierarchy

34. 34
{
"_id" : ObjectId("5277f00e8de2b30a03d9b8b2"),
"book_id" : "Book_1_eq_ftse",
"parent_book_id" : ”Book_1_eq",
”ancestors" : [
“Book_1_eq”,
“Book_1”
],
"isLeaf" : true
}
Risk Repository
Representing Hierarchy
db.ensureIndex({…,ancestors:1})
db.hierarchy.find({ ancestors: “Book_1” })

35. 35
{
"_id" : ObjectId("527f505f3004da4f4e5b53d2"),
"pkg_id" : 1,
"book_id" : "Book_1",
"cob_date" : ISODate("2013-12-10T00:00:00Z"),
"report_status" : "O",
"risk_factor" : "ftse100",
"trigger_file" : "fileName",
"pnl" : [
{ m : 1, v : 1234.34 },
{ m : 2, v: 2211.22 },
…………
…………
]
}
Risk Repository
Representing Packages

36. 36
db.pkg.aggregate(
{ $match : {book_id:{$in:[<book_id_list>]}, "risk_factor":"ftse100"} },
{ $group:{_id:{"cob_date":"$cob_date", "report_status":"$report_status"},
"temparray":{$push:{"book_id":"$book_id","pnl":"$pnl"}}} },
{ $sort:{"_id.cob_date":-1} },
{ $unwind:"$temparray" },
{ $unwind:"$temparray.pnl" },
{ $group:{ "_id":{"cob_date":"$_id", "mcrun":"$temparray.pnl.r"},
"var":{$sum:"$temparray.pnl.v"}} },
{ $project:{"_id":0,"var":1} },
{ $sort:{var:-1} },
{ $skip:100 },
{ $limit:1 }
)
Sort by var
Skip 100 records (1%)
Read of VaR
Group by MC Run Id
List of Book’s in Hierarchy
Risk Repository
Aggregating VaR

37. 37
Thank You

38. 38
For More Information
Resource Location
MongoDB Downloads mongoDB.com/download
Free Online Training Education.mongoDB.com
Webinars and Events mongoDB.com/events
White Papers mongoDB.com/white-papers
Case Studies mongoDB.com/customers
Presentations mongoDB.com/presentations
Documentation docs.mongodb.org
Additional Info info@mongoDB.com
Resource Location

40. 40
• Event Driven Architecture
• Intelligent application design
open_position{
"Asset_Type" : "Equity"
"Exchange Currency" : "USD"
"Underlying" : "IBM"
"Exchange" : "Nasdaq”
“Date_History” : {“03/10/13’, “02/10/13”, “01/10/13”, “30/09/13” ………}
”Price_History” : {”184.96”, ”183.00”, ”185.12”, ”181.00”, ”179.00”………..}
}
Aggregated Risk on Demand
Risk on Demand != Brute Force
Marketdata
MTM
Portfolio
Mapping
Trades
VaR

41. 41
Source Layer BI Abstraction &
Reporting Layer
Acquisition Layer
Extraction &
Staging
Cleansing
Atomic Layer
MDM
Ad-hoc reports &
Analytics
Dashboards &
Web Reports
Web Services
Corporate Data Warehouse
Data Lineage and Metadata
ETL
Transformation & Access
Layer
Transformation &
Calculation
Performance &
Access
Change Data
!
Reject Data
Data
not
null
Data
within
range
Data
in right
format
Normalisation
& Storage
FS/Banking Challenges
1. Changing Regulatory Requirements