Transform Banking with Big Data and Automated Machine Learning 9.12.17

Confidential. Copyright © DataRobot, Inc. and Cloudera, Inc. - All Rights Reserved
How to Dominate Banking with
Artificial Intelligence
Greg Michaelson, PhD
VP, DataRobot Labs
Steve Totman
Industry Leader, Financial Services,
Cloudera

We believe data can make
what is impossible today,
possible tomorrow

1. Introductions
2. The opportunity
3. How to be successful with data science
4. DataRobot Demo
5. Next Steps (what to do after the webinar)
Agenda

$124M
140+
IN FUNDING
200,000,000+
MODELS BUILT ON
DATAROBOT CLOUD
I N S U R A N C E B A N K I N G H E A L T H C A R E F I N T E C H M A N Y M O R E
#1 RANKED
DATA SCIENTISTS
4
50+
TOP 3 FINISHES
The world’s most advanced Enterprise Machine Learning platform
DATA SCIENTISTS &
ENGINEERS (OF 260+)
2012FOUNDED
HQ in Boston, MA

5© Cloudera, Inc. All rights reserved.
2008
founded
Big data
innovators from
Google, Yahoo
and Oracle
Cloudera at-a-glance
Customer success
Large enterprises fueling growth
48%
customer
growth
Last 4 years
Global 8000
customers
140%+
net
expansion
Expansion driven
by data and new
use cases
Open partner network
Best of breed
solutions
3000+
Partners
Vast ecosystem
of solution &
service providers
First to market
Open source innovation
1600+
Clouderans
Global team doing
business in
28 countries
NYSE: CLDR

Cloudera - Adoption driven by large enterprises
1000+ customers
across all verticals
~500 Global 8000
customers
7/10 9/1
0
2
9
6/1
0
8/10Top Global Top Global Top Global Top GlobalCountries with
government
customers
BANKING TELCO PUBLIC HEALTHCARE TECHNOLOGY

Every aspect of financial services is an AI opportunity
Upsell/Cross-sell Customer loyalty, retention
CUSTOMER JOURNEY / 360
Pricing analytics Churn reduction
OPERATIONS
Loan review
COMPLIANCE HUB
Loss forecasting/
Stress Testing
PRODUCT & SERVICE EFFICIENCIES
Investment research and client
targeting
Portfolio analytics and robo-
advising
REVENUE GENERATION
Prospecting Marketing
RISK / FRAUD
Delinquent asset resolution Credit and prepayment risk
Model Governance
and Validation
KYC/AML
Underwriting
Succession
planning
Fraud reduction (ATM, card, merchant services)
Resolve with
previous slide

Behold! The Fintech Invasion

1. You have the right people and
the right data today
2. Teach everyone to spot
opportunities to leverage AI
3. Think Big, Start Many, Iterate
Often
4. Execution includes
implementation
5. Practice Safe data science -,
Secure, Governed, Legal, Ethical
9
5 Keys to Success
with Data Science

You have the right people
and the right data today

Your Big Data Team is Ready for AI Today
Analytics
Data
Management
Infrastructure
Your Data Organization today can do
this - start with the data and people
you have today and iterate
Shared Data
Platform
Architects
Data Scientists
Data Engineers
critical
these
roles be
tightly
aligned
Legacy Today

The Executive Sponsor Technical staffThe Business Champion
The Data Science Line-up

The executive sponsor:
● Initiates, prioritizes, and drives data science solutions
● Advocates for an organizational mastery of data science
● Hires the right people; chooses the right technologies; and
makes the right partners
● Cuts through red tape, bureaucracy, and hype
● Insists on collaboration between business and techies
Common mistakes:
● Forgetting about implementation until the end
● Being intimidated by academics, statisticians, and data
scientists
● Being vague about expectations and output
● Failing SLOW or giving up QUICK
The Data Science Line-up: Executive Sponsor

The business champion:
● Communicates context, importance, and constraints
● Subject matter expert for the business
● Eagerly embraces technology and tools
● Fully participates in planning and testing
● Evaluates results and makes course corrections
Common mistakes:
● Not asking enough questions
● “Checking out” when things get technical
● Playing go-between between techies and business
The Data Science Line-up: Business Champion

The technical staff:
● Ensure proper data practices
● Think hard about presentation and communication
● Build repeatable, accurate, practical solutions
● Different skill sets: data assembly, data science,
implementation
Common mistakes:
● Not learning the business
● Making communication too technical
● Ignoring business feedback
● Delivering impractical or too slowly
The Data Science Line-up: Technical Team

Teach everyone
to spot opportunities
to leverage AI

Identifying Opportunities in Your Business
People who understand
your business
Data Scientists
Your employees

Low ROI, few ideas, limited business impact
your business
Data Scientists
Your employees

You don’t need to be a data scientist to spot opportunities
your business
Data Scientists
Your employees

Think Big
Start Many
Iterate Often

PREREQUISITES
1. Knowledge of the overall & specific missions
2. Knowledge of the data
3. Ability to write code to gather data
4. Ability to write code to explore/inspect data
5. Ability to write code to manipulate data
6. Ability to write code to extract actionable intel
7. Ability to write code to build models
8. Ability to write code to implement models
9. Foundational statistics
10. Internals of algorithms
11. Practical knowledge and experience
12. Knowing how to interpret and explain models
The AI Bottleneck: Data Scientists
Math &
Stats
Domain
Expertise
Programming
Skills

PREREQUISITES
1. Knowledge of the overall & specific missions
2. Knowledge of the data
Machine Learning Automation: The New Prerequisites
Math &
Stats
Domain
Expertise
Programming
Skills
AUTOMATED

Advanced tools make business people more technical
and technical people more business focused
Utilizing the Right Tools is the Key
Data Preparation and Munging Visualization
Data Science
Shared Data Platform

Execution includes implementation

80% 20%30% 10% 25% 35%
#lie Data Science is 80% Data Preparation
DATA PREPARATION MODELING
MODEL RISK MANAGEMENT /
AUDIT / VALIDATION
INTEGRATION AND IMPLEMENTATION
faster modeling > faster data prep > faster, better results
better modeling > less time spent validating > faster, better results
better tools > faster and cheaper implementation > faster, better results
(... in other words, the real problem is not building the models…)

Practice safe data science

27© 2016 Cloudera, Inc. All rights reserved.
Data Free-for-All:
Available & Error-Prone
Basic Security Controls:
Authorization
Authentication
Comprehensive Auditing
Data Security &
Governance:
Lineage Visibility
Metadata Discovery
Encryption & Key
Management
Practice Safe Data Science - Secure
Start with the Big Data Security Maturity Model
Achieve Scale and Cost Effectiveness via a Secure Data Platform
Fully Compliance Ready:
Audit-Ready & Protected
Audit Ready For:
EU Data Protection Directive,
PCI DSS
HIPAA, FERPA
FISMA, PII
Full encryption, key
management, transparency,
and enforcement for all data-
at-rest and data-in-motion
DataVolume&Sensitivity
Security Compliance & Risk Mitigation
0 Highly Vulnerable
Data at Risk
1 Reduced Risk
Exposure
2 Managed, Secure,
Protected
3 Enterprise Data
Hub Secure Data
Vault

Data Governance Council
Cross-Org: has the authority for governing data
Data Stewards / Guardians
Retention
Management
Profile
Management
Quality
Management
Data
Privacy
Step 1: Data engineering team proposes policies, data steward roles, data
dictionary, master data (for profiles)
Step 2: Employ technology, e.g. Cloudera Navigator, to implement policies
Step 3: Data governance exec council – cross company participation
Data Governance Program
Responsible for governing the company’s Big Data Asset
Practice Safe Data Science - Data Governance “Lite”

Practice Safe Data Science- Ethical Data Usage
Good way to evaluate use cases -
What would mum and dad think?

Behind every machine learning success story,
there is a data success story.

<1%
of unstructured data
is analyzed or
used at all
<50%
of structured data
is actively used in
making decisions
>70%
of employees have
access to data they
should NOT
Extracting value from data – an untapped opportunity

The enterprise platform for machine learning
PATTERN
RECOGNITION
DETECTION
PREDICTION
650+CUSTOMERS RUN
ON
DRIVE CUSTOMER INSIGHTS
Market segmentation
Customer 360
Next best offer
Churn analysis & prevention
PROTECT BUSINESS
Cybersecurity
Fraud
Anti-money laundering
Risk modeling & assessment
SPAM detection
CONNECT PRODUCTS & SERVICES
(IoT)
Predictive maintenance
Genomics & personalized
medicine
Predicting and preventing disease
Natural language

OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Encrypt and KeyTrustee
STRUCTURED
Sqoop
UNSTRUCTURED
Kafka, Flume
PROCESS, ANALYZE, SERVE
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
Sentry, RecordService
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
DATA
SCIENCE
CDSW
SEARCH
Solr
SQL
Impala
NoSQL
HBase
OTHER
Object Store
FILESYSTEM
HDFS
RELATIONAL
Kudu
Cloudera options for data science and machine learning

DataRobot + Cloudera: Better Together
DataRobot on Cloudera Enterprise
increased experience:
• Spark on Hadoop for distributed
model scoring
• Cloudera Manager integration to
easily deploy and monitor
DataRobot jobs
• Certified on secure cluster to deploy
the joint solution confidently
• Leverage DataRobot libraries from
Cloudera Data Science Workbench
DATA
MANAGEMENT
Cloudera Navigator
Encrypt and KeyTrustee
STRUCTURED
Sqoop
UNSTRUCTURED
Kafka, Flume
PROCESS, ANALYZE, SERVE
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
Sentry, RecordService
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
DATA
SCIENCE
CDSW
SEARCH
Solr
SQL
Impala
NoSQL
HBase
OTHER
Object Store
FILESYSTEM
HDFS
RELATIONA
L
Kudu
OPERATIONS
Cloudera Manager
Cloudera Director
Integration Point

Demo

Call to Action
Learn more about the Cloudera Fast Forward Lab in this blog by Cloudera’s Chief
Strategy Officer Mike Olson:
http://vision.cloudera.com/fast-forward/
Visit Cloudera Financial Services Solutions page:
https://www.cloudera.com/solutions/financial-services.html
Check out these DataRobot banking solutions:
https://www.datarobot.com/banking/

Appendix

Transform Banking with Big Data and Automated Machine Learning 9.12.17

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Transform Banking with Big Data and Automated Machine Learning 9.12.17