It is imperative that Financial Services firms align the organization around providing maximum value to customers across all channels and products with the agility to capitalize on new opportunities. They must do this at the same time as cutting costs, improving operational efficiency, and complying with current and future regulations. This effort is commonly referred to as Industrialization, or streamlining people, process, and technology for maximum customer value, service, and efficiency. MongoDB can help you in this initiative by allowing you to centralize data management no matter how it is structured across channels and products and make it easy to aggregate data from multiple systems, while lowering TCO and delivering applications faster. MetLife publicly announced that they used MongoDB to enable a single view of the customer in 3 months across 70+ existing systems. We will explore case studies demonstrating these capabilities to help you industrialize your firm. Key takeaways: Unique capabilities, brought to you by MongoDB Concrete use cases that help industrialization Implementation case studies, to pave the way

1. Achieving Customer Centricity and
High Margins in Financial Services
with MongoDB
Matt Kalan
Business Architect, Financial Services
matt.kalan@mongodb.com
@matthewkalan

2. FS Industry transformation
Drivers of change
Requirements
•
Lost revenue (fees, prop
trading)
•
New products and new
markets
•
New competitors
•
Increase wallet share
•
Better risk management
•
Cost savings
•
Emerging markets
opportunities
•
Agility to respond to
competitors & regulators
•
Regulatory change and
uncertainty
•
Cross-channel and
global integration
•
Proliferation of channels
•
•
Globally distributed
operations
Intraday decision
support
•
Operational efficiencies
•
Faster market
movements
2

3. Challenges with Current Structure
Customers
Web
Impact
• Similar processes
and systems
duplicated
Central
Functions
• Risk
• Compliance
• Legal
• …
• Changes done in
multiple places
• Siloed view of
customer
• Siloed experience by
customer
• Cross-channel/silo
data is previous day
3
Cards
…
…
Deposit
Accounts
Call
Center
Loans

4. Current sample user scenario
• Start to apply for credit card on web
• Have a question; look up customer service
number
• Call number, wait in queue, it’s the wrong call
center, re-route to correct one
• Wait in another queue, get to agent
• Agent answers question and starts all over in
applying for credit card
• Ask me about a balance transfer for the 100th
time
4

5. Industrialization: customer centricity
while optimizing people, process, & tech
Customers
Solution: Reuse processes
and technology services
across product
Mobile
Real-time cross-channel &
product decision support
Loans
Cards
Right action at the right
time in right channel
Benefits
•
Increased wallet share
•
Operational
efficiencies
•
…
Higher customer
satisfaction
•
Shared services
• Single View of Customer
• Cross-sell
• Onboarding
• Customer Service
• Fraud detection
• Intraday Risk
• Compliance
Faster product rollout
…
Computer
5
ATM

6. Future user scenario
• Start to apply for credit card on web/mobile
• Chat is available but I have time so call agent
• Automated line knows my history
• Offers to route me directly to agent
• Agent continues where I left off
• Ask me about a mortgage for which I scanned a
QR code today walking by a branch
6

7. Obstacles for Enabling
Industrialization Today
1. Aggregation of disparate data is difficult
2. Legacy systems often not real-time enabled
3. Master data can be hard to change and distribute
4. Operational applications are siloed
5. Application development is not agile
7

8. Difficult because RDBMSs not
supporting modern requirements
Data Types & OOP
Agile Development
• Unstructured data
• Iterative
• Semi-structured
data
• Short development
cycles
• Polymorphic data
• New workloads
Volume of Data
New Architectures
• Petabytes of data
• Horizontal scaling
• Trillions of records
• Commodity
servers
• Millions of queries per
second
8
• Cloud computing

9. What if the database enabled agility
more than limited it?
• Dynamic and variable schemas
• Richly-structured data
• Easy horizontal scaling
• Low TCO
• Plus still maintaining old capabilities
– Rich querying
– Strongly consistently data
9

10. Richly structured and dynamic
schema
Relational
Document Data Structure
{
}
10
customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
accounts : [
{
account_number : 13,
branch_ID : 200,
account_type : "Checking"
},
{
account_number : 14,
branch_ID : 200,
account_type : ”IRA”,
beneficiaries: […]
}]

11. MongoDB capabilities
1. Dynamic Document
Schema
Application
2. Native language drivers
db.customer.insert({…})
db.customer.find({
name: ”John Smith”})
{ name: “John Smith”,
date: “2013-08-01”),
address: “10 3rd St.”,
phone: [
{ home: 1234567890},
{ mobile: 1234568138} ]
}
Driver
Mongos
3. High availability
Shard 2
Shard N
Primary
Primary
Primary
Secondary
Secondary
Secondary
- Replica sets
Shard 1
Secondary
…
5. Horizontal scalability
11
- Sharding
Secondary
Secondary
4. High performance
- Data locality
- Indexes
- RAM

12. Current Challenges and
Case Studies of Solutions

13. Challenge: Aggregation of disparate
data is difficult
Batch
Datamar
t
Batch
Datamar
t
Customer
Accounts
Loans
Loans
Loans
…
Deposits
Deposits
Deposits
13
Batch
Datamar
t
Batch
Data
Warehouse
Reporting
Cards
Cards
Cards
Issues
• Yesterday’s data
• Details lost
• Inflexible schema
• Slow performance
Impact
• What happened today?
• Worse customer
satisfaction
• Missed opportunities
• Lost revenue

14. Solution: Using dynamic schema and
easy scaling
Operational Data Hub
Customer
Accounts
CSR Application
Real-time or
Batch
Customer Portal
Loans
Loans
Loans
…
…
Deposits
Deposits
Deposits
14
Operational
Reporting
Data
Warehouse
Strategic
Reporting
Cards
Cards
Cards
Benefits
• Real-time
• Complete details
• Agile
• Higher customer
retention
• Increase wallet share
• Proactive exception
handling

15. Single View Case Study:
Tier 1 Global Insurance Provider
Global 360 degree view of customers’ policy portfolio
and interactions
Problem
Why MongoDB
Results
• 70 systems and 20
screens to view
customer policies
• Dynamic schema: can
combine 70 systems
easily
• Many CSR calls taken
just to reroute customer
• Performance: can handle • Unified customer view
all data in one DB
available to all channels
• Poor customer
experience
• Replication: local reads
and high availability
• Shorter and less calls rerouted
• Source systems are
hard to change
• Sharding: can add data
easily by scaling out
• Increased customer
satisfaction
15
• Delivered in 3 months
with $3M – previous
attempts failed over 2 yrs

16. Challenge: Legacy systems often not
real-time enabled or too slow
Data
source 1
Batch copy
Application 1
Often not ready to expose as
enterprise services
• Mainframe
• Core systems
• Data Warehouses
• Not scalable system
Application 2
Data
source 2
…
Slow
request/response
16
Application 3
…
Data
source N
Batch copying of data many
times or requests are too slow
Application X
Changing source data affects X
systems
Impact
• Slow time to market
• Resource intensive
• Hard to change interfaces and
modernize system

17. Solution: Virtualize legacy systems
with a persistent caching service
Mainfram
e
Batch
Batch copy
API
Batch copy
Application 1
Application 2
EDW
…
…
Pub/sub
…
Core
system
Application 3
Application X
17
Benefits
• Faster time to market
• More agile in changing
sources
• Can modernize data sources
behind virtualization
• Infinite scale with low TCO

18. Case Study: Global Custodial Bank
Virtualize Enterprise Data Sources
Create a central data hub for accessing data across
the enterprise
Problem
• Found numerous pointto-point copies of data
• Change in one system
impacts multiple groups
• Response time on EDW
was too slow
• Wanted one central
data hub for most often
accessed data
18
Why MongoDB
Results
• Dynamic schema: can
• Data accessible by batch
normalize data as needed or REST layer in one place
and prioritized
• Customer portal response
• Performance: can handle times shrunk by 90%
all data in one logical DB
• Shorter development times
• Sharding: can add data
with more accessible hub
easily by scaling out
• Could modernize data
sources without changing
apps

19. Challenge: Master data can be hard
to change and distribute
Batch
Batch
Batch
Golden
Copy
Common issues
• Hard to change schema
of master data
• Data copied everywhere
and gets out of sync
Batch
Batch
Batch
Batch
Batch
Impact
• Process breaks from out
of sync data
• Business doesn’t have
data it needs
• Many copies creates
19
more management

20. Solution: Persistent dynamic cache
replicated globally
Real-time
Real-time
Real-time
Real-time
Real-time
Solution:
• Load into primary with
any schema
• Replicate to and read
from secondaries
Real-time
Real-time
Real-time
Benefits
• Easy & fast change at
speed of business
• Easy scale out for one
stop shop for data
• Low TCO
20

21. Case Study: Global bank
Reference Data Distribution
Distribute reference data globally in real-time for
fast local accessing and querying
Problem
• Delays up to 36 hours in
distributing data by batch
• Charged multiple times
globally for same data
• Incurring regulatory
penalties from missing
SLAs
• Had to manage 20
distributed systems with
same data
21
Why MongoDB
Results
• Dynamic schema: easy to • Will save about
load initially & over time
$40,000,000 in costs and
penalties over 5 years
• Auto-replication: data
distributed in real-time,
• Only charged once for data
read locally
• Data in sync globally and
• Both cache and database: read locally
cache always up-to-date
• Capacity to move to one
• Simple data modeling &
global shared data service
analysis: easy changes
and understanding

22. Reporting
Reporting
Loan
Transactions
Loan Systems
Deposit
Transactions
22
…
Card Systems
…
Cards
Transactions
Reporting
Challenge: Siloed operational
applications
Deposit Systems
Impact
• Views are siloed
• Duplicate management
and data access layer
• Need another layer to
aggregate

23. Solution: Unified data services
Loan Systems
…
…
…
…
Common persistence framework
Reporting
Card Systems
Deposit Systems
23
Benefit
• Each application can
still save its own data
• Data is already
aggregated for crosssilo reporting
• One cluster and data
access layer to manage

24. Case Study: Global Broker Dealer
Trade Mart for all OTC Trades
Distribute reference data globally in real-time for
fast local accessing and querying
Problem
• Each application had its
own persistence and
audit trail
• Wanted one unified
framework and
persistence for all
trades and products
• Needed to handle many
variable structures
across all securities
24
Why MongoDB
Results
• Dynamic schema: can
• Fast time-to-market using
save trade for all products the persistence framework
in one data service
• Store any structure of
• Easy scaling: can easily
products/trades without
keep trades as long as
changing a schema
required with high
• One consolidated trade
performance
store for auditing and
reporting

25. Challenge 5: Application development
not agile enough
Requirement changes
Change
25

26. Solution: Match dynamic data model
to the application
26

27. 70%+ Lower TCO
$1,680K
Dev. and Admin
Commercial RDBMS
$517K
Compute – Scale-Up Servers
Dev. and Admin
Storage – SAN
Compute – Commodity HW
Storage – Local Storage
27

28. Summary
• FS firms are targeting industrialization to return
revenue and margins to historical levels
• IT needs to help this endeavor by enabling agility
& customer centricity across all products &
channels
• We discussed 5 concrete examples to begin
industrializing to drive business value today
• Many of your competitors have already delivered
on the value of MongoDB so you can too!
28

29. MongoDB Products and Services
Subscriptions
MongoDB Enterprise, Monitoring, Support, Commercial License
Consulting
Expert Resources for All Phases of MongoDB Implementations
Training
Online and In-Person for Developers and Administrators
MongoDB Monitoring Service
Free, Cloud-Based Service for Monitoring and Alerts
MongoDB Backup Service
Cloud-based service for backing up and restoring MongoDB
29

30. 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
30
Location
info@mongodb.com