Real-time risk management coupled with the requirements for regulatory reporting are top of mind for many heads of risk. In this webinar, we will cover how MongoDB can help with: Implementing proactive risk controls Aggregated Risk on Demand Creating an Adaptive Regulatory Reporting Platform Cost Effective Risk Calculations

1. Real-Time Risk Management &
Regulatory Reporting
- Jim Duffy: Business Architect Global Financial
Services
- Kunal Taneja: Solutions Architect Financial Services

2. Agenda
• The Challenges
• Evolution of Information Management in Finance
• Common Positioning of mongoDB for Risk & Regulatory
• A bit about mongoDB terms
• How our cluster topology addresses Risk & Regulatory
Challenges
• Aggregated Risk on-Demand
2

3. FS/Banking Challenges
Changing Regulatory Requirements
SWAPS Push
Volker Rule – Out – Dodd
EU Reg on
Dodd Frank Frank
Credit
Recovery &
Rating
Resolution
EMIR
Agencies
EU
Transparency
Directive
PRIP
Short
Selling
2012
Crisis
Management
2013
PD
Close Out
Netting
Securities
Law
Directive
(SLD)
3
FATCA
CRDV
Financial
Transaction
Tax
Cross
ICB /
Competition
ICB Ring-fencing
Border Debt
Recovery
2014
ICB Loss
Absorbency
MiFID II
T2S
2015
Internal
Governance
Audit
Guidelines
Accounting
Policy
AIFM
Directive
Directive
Market Review
Abuse
Directive
(MAD II)
2016
LCR –
Basel III
2017
NSFR –
Basel III
2018
2019
Leverage
Ratio Basel III

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

5. Evolution of Information Management
Risk Compute Grid
(VaR)
Swaps
Regulatory
Reporting
Platform
Rates
Market Abuse
and Compliance
Equities
Markets, MTFs, Internal
Liquidity, etc
5
Derivatives

6. Asset Class Silos
Risk Compute Grid
(VaR)
Regulatory
Reporting
Platform
Market Abuse
and Compliance
Reference
Data
Reporting
Reporting
Reporting
Reporting
Warehouse /
Repository(s)
Warehouse /
Repository(s)
Warehouse /
Repository(s)
Warehouse /
Repository(s)
Operational Data
Store(s)
Operational Data
Store(s)
Operational Data
Store(s)
Operational Data
Store(s)
Operational
Systems
Operational
Systems
Operational
Systems
Operational
Systems
Swaps
Rates
Equities
Derivatives
Markets, MTFs, Internal
Liquidity, etc
6

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

8. Cross Asset Class Caching Layer
Risk Compute Grid
(VaR)
Regulatory
Reporting
Platform
Market Abuse
and Compliance
Cross Asset
Data
Warehouse
Reference
Data
Data Services / Reporting
In Memory Cache, Replication and Relational Database Technology
Operational Data Store(s)
Operational Data
Store(s)
Operational Data
Store(s)
Operational
Systems
Operational
Systems
Operational
Systems
Operational
Systems
Swaps
Rates
Equities
Derivatives
Markets, MTFs, Internal
Liquidity, etc
8

9. mongoDB as an Operational Data Layer
Risk Compute Grid
(VaR)
Regulatory
Reporting
Platform
Market Abuse
and Compliance
Cross Asset
Data
Warehouse
Reference
Data
Data Services / Reporting
Operational Data Layer (ODL)
Operational
Systems
Swaps
Operational
Systems
Rates
Operational
Systems
Equities
Markets, MTFs, Internal
Liquidity, etc
9
Operational
Systems
Derivatives

10. What is an ODL?

11. 4 Important Terms
• Shard: Essentially a partition of horizontally scaling data
• Replica: Copies of data for high availability, redundancy
and work load isolation
• Shard Tagging: Method of dispatching data in a cluster
• Replica Tagging: Method of isolating work loads in a
cluster
11

12. mongoDB Terminology
Secondary
Secondary
Primary
US
EU
Swaps
12
Asia
US
EU
Rates
Asia
US
EU
Asia
Equities
US
EU
Asia
Derivatives

13. mongoDB Terminology
Secondary
Secondary
Primary
US
EU
Swaps
13
Asia
US
EU
Rates
Asia
US
EU
Asia
Equities
US
EU
Asia
Derivatives

14. mongoDB Terminology
Shard: A subset of a horizontally scaling data set
Shard
Secondary
Secondary
Primary
US
EU
Swaps
14
Asia
US
EU
Rates
Asia
US
EU
Asia
Equities
US
EU
Asia
Derivatives

15. mongoDB Terminology
Shard: A subset of a horizontally scaling data set
Replica: A copy of a data set for high availability,
redundancy and work load isolation
Shard
Replica
Secondary
Secondary
Primary
US
EU
Swaps
15
Asia
US
EU
Rates
Asia
US
EU
Asia
Equities
US
EU
Asia
Derivatives

16. mongoDB Terminology
Shard Tagging: Dispatches writes by asset class and geography
Secondary
Secondary
Primary
US
EU
Asia
Swaps
16
US
EU
Rates
Asia
US
EU
Asia
Equities
Shard Tag By Asset Class and Geography
US
EU
Asia
Derivatives

17. mongoDB Terminology
Shard Tagging: Dispatches writes by asset class and geography
Replica Tagging: Ensures isolation of work loads
Replica Tag dedicated to the Intraday VaR data service
Secondary
Secondary
Primary
US
EU
Asia
Swaps
17
US
EU
Rates
Asia
US
EU
Asia
Equities
Shard Tag By Asset Class and Geography
US
EU
Asia
Derivatives

18. mongoDB in the context of Risk

19. Active Risk Control Framework
Task: Implement globally consistent active risk controls while
maintaining local governance of asset class specific controls
Secondary
Secondary
Primary
US
EU
Swaps
19
Asia
US
EU
Rates
Asia
US
EU
Asia
Equities
US
EU
Asia
Derivatives

20. Active Risk Control Framework
Task: Implement globally consistent active risk controls while
maintaining local governance of asset class specific controls
Blacklisted instruments centrally controlled and monitored
Secondary
Secondary
Primary
US
EU
Swaps
20
Asia
US
EU
Rates
Asia
US
EU
Asia
Equities
US
EU
Asia
Derivatives

21. Active Risk Control Framework
Task: Implement globally consistent active risk controls while
maintaining local governance of asset class specific controls
Blacklisted instruments centrally controlled and monitored
Secondary
Secondary
Primary
US
EU
Swaps
21
Asia
US
EU
Rates
Asia
US
EU
Asia
Equities
Asset Class specific controls locally governed
US
EU
Asia
Derivatives

22. Adaptive Regulatory Reporting
Task: Implement a cross asset class regulatory reporting platform
which will keep pace with change and enable a 360 degree view of risk
Secondary
Secondary
Primary
US
EU
Swaps
22
Asia
US
EU
Rates
Asia
US
EU
Asia
Equities
US
EU
Asia
Derivatives

23. Adaptive Regulatory Reporting
Task: Implement a cross asset class regulatory reporting platform
which will keep pace with change and enable a 360 degree view of risk
MiFID2
Dodd-Frank
Secondary
Secondary
Primary
US
EU
Swaps
23
Asia
US
EU
Asia
Rates
Libor Review, What’s coming?
US
EU
Asia
Equities
US
EU
Asia
Derivatives

24. 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
24

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

26. Aggregated Risk on Demand
• Regulators are pushing for “better” Risk
aggregation capabilities in banking post 2007
26
http://www.bis.org/publ/bcbs239.pdf

27. 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”
Secondary
Secondary
Primary
US
EU
Bonds
27
Asia
US
EU
Rates
Asia
US
EU
Equities
Asia
US
EU
Derivatives
Asia

28. 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 ….”
VaR
Calculator
Cross Asset
Data
Warehouse
Extract – Transform - Load
Time??
Operational
Systems
Operational
Systems
Operational
Systems
Operational
Systems
Bonds
Rates
Equities
Derivatives
28

29. Aggregated Risk on Demand
Historical Simulation
•
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)
29

30. Aggregated Risk on Demand
Why MongoDB?
• 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
30

31. Aggregated Risk on Demand
Why MongoDB?
Risk Application
(Historical Simulation)
Quant
Library
Primary
Operational
Systems
31
Operational
Systems
Operational
Systems
Operational
Systems
Bonds
Rates
Equities
Derivatives

32. An approach with Monte Carlo Sim
Representing Hierarchy
“Udf_h1”
32

33. Risk Repository
Representing Hierarchy
{
"_id"
: ObjectId("5277f00e8de2b30a03d9b8b2"),
"book_id" : "Book_1_eq_ftse",
"parent_book_id" : ”Book_1_eq",
”ancestors" : [
“Book_1_eq”,
“Book_1”
],
"isLeaf" : true
}
33
db.ensureIndex({…,ancestors:1})
db.hierarchy.find({ ancestors: “Book_1” })

34. Risk Repository
Representing Packages
{
"_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,
…………
…………
]
}
34
v: 2211.22 },

35. Risk Repository
Aggregating VaR
db.pkg.aggregate(
List of Book’s in Hierarchy
{ $match : {book_id:{$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"}}} },
Group by MC Run Id
{ $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} },
Sort by var
{ $skip:100 },
Skip 100 records (1%)
{ $limit:1 }
)
35
Read of VaR

36. Thank You
36

37. For More Information
Resource
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
37
Location
info@mongoDB.com

39. Aggregated Risk on Demand
Risk on Demand != Brute Force
• Event Driven Architecture
• Intelligent application design
Marketdata
Trades
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”………..}
Portfolio
Mapping
}
MTM
VaR
39

40. FS/Banking Challenges
1. Changing Regulatory Requirements
ETL
Corporate Data Warehouse
Corporate Data Warehouse
Source Layer
Source Layer
Acquisition Layer
Acquisition Layer
Extraction &
Staging
Cleansing
Atomic Layer
Atomic Layer
Normalisation
& Storage
Transformation && Access
Transformation Access
Layer
Layer
Transformation
& Calculation
Change Data
Performance &
Access
BI Abstraction &&
BI Abstraction
Reporting Layer
Reporting Layer
Web Services
Dashboards &
Web Reports
MDM
Ad-hoc reports &
Analytics
!
Reject Data
40
Data Lineage and Metadata