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1. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
ANALYTICS: WHAT IS IT REALLY, AND HOW
CAN IT HELP MY ORGANIZATION?
A FINTECH SUCCESS STORY ON ANALYTICS
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2. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
ANALYTICS
an·a·lyt·icsˌanəˈlidiks/ noun
Analytics is the discovery and communication of meaningful
patterns in data. Especially valuable in areas rich with recorded
information, analytics relies on the simultaneous application
of statistics, computer programming and operations research to
quantify performance. Analytics often favors data visualization to
communicate insight.
- Wikipedia 2015

3. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
ANALYTICS
Analytics solve business problems by exploring
an idea with data.
Are no longer a science experiment.
- Steve Holder
an·a·lyt·icsˌanəˈlidiks/ noun

4. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
Exploration
Data Visualization
ANALYTICS WHAT DOES ANALYTICS MEAN TO YOU?
Traditional Reporting
Creation and sharing of known
information resulting in the delivery of
reports
Data Discovery
Exploration of data to
discovery new questions and
opportunities
Advanced Analytics
Leverage Data to make the best
decision
Forecasting
Optimization
Prediction
Standard Reports
Dashboards
Ad Hoc Query

5. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
RANGE OF
ANALYTICS

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Idea
Idea
ANALYTICS
Explore and
Get Value Now
Relationship
Difference
Trend
Prediction
Advanced and Predictive Analytics Software
Revenue Vendor Share
SAS
IBM
Microsoft
35.4 %
17.1 %
3.0 %
May 2014

7. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
ANALYTICALLY
IMMATURE
ANALYTICALLY
AWARE
ANALYTICALLY
INFORMED
ANALYTICALLY
RELIANT
ANALYTICALLY
INNOVATIVE
LEVEL 1
LEVEL 2
LEVEL 3
LEVEL 4
LEVEL 5
Isolated
analytics use.
Unsophisticated
tools and
practices
predominate
Predictive analytics
usage is part of
mission critical
applications only.
Full benefits are not
understood by a
majority in the
organization.
Analytics usage
consists primarily of
tactical and ad hoc
approaches.
Analytics dev. and
deployment is
constrained, yet
departments have
their own experts
and/or initiatives.
Analytics talent
is centralized into
larger groups.
Management
understands and
supports analytics
for strategic value,
thus bringing
business units into
alignment
Company is
committed to
analytics as part of
its future growth
plan.
Business units
embrace their own
transformational
analytical plans.
ANALYTICS USAGE ANALYTICS DEVELOPMENTAL LEVELS

8. 8
SAS Webinar
December 8th, 2015

9. Underwriting Process
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10. Mogo Credit Programs
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MogoLiquid MogoMini MogoZip
Better credit profiles Alternative to payday loans Emergency advance loans
Max Loan $35,000 $2,500 $1,500
Term 1 – 5 years 1 year 2 weeks / 30 days
Rate As low as 5.9% ~39.9% – up to 93% lower than
payday loans
Up to 50% lower than payday
loans

11. Peggys Cove, NS
Predictive Risk Analytics is proving
critical for online lending:
Mogo hired a credit risk team
and leveraged risk analytics
for credit decision and KYC

12. http://linearpopulationmodel.blogspot.ca/2015/01/on-internet-nobody-knows-youre-dog.html

13. Analytics on Mogo Credit Process
13
Application

Fraud Detection
and KYC

Credit Decision

Verification

Funding

 Application
• www.mogo.ca/apply
 Fraud Detection / KYC
• ReD Tool Algorithms: Device Recognition,
IP Proxy Detection, GEO Location, Fraud
rule creation, management and decision
• Equifax eID Verifier: Customers identifies
themselves answering unique questions
based on their credit history
• OSFI’s Designated Persons bit.ly/1UpxAJ1
• Mogo is working to incorporate big data
from social networks / enhanced data to
reduce fraud exposure
 Credit Decision
• Decision drivers: Equifax Risk Score and
Custom Mogo Score
 Verification
• Varies by product and by a verification
score based on ReD, Equifax Safescan
and Credit Limit

14. Mogo’s journey  Analytically Driven
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• Mogo’s vision, sponsored by senior management, our culture!
• Leading edge, innovative Risk Management
– Continuous improvement mindset with rapid development
– Leverage SAS Enterprise Miner as data mining analytical tool
– In 2012 brought new COO, with extensive analytics background; hired
data scientists with large industry expertise to create predictive
models for Canadian Customers
• Onboarding is a digital experience: Customer fills the online
application and provide ID/supporting documents
• Full spectrum loans  high approval rates / Customer is offered
the right loan for the credit profile
• Socially responsible lending: models drive better pricing and the
exclusive Level Up program¹
¹ Lower rates after good credit behavior

15. Example of Predictive Modeling
based on Risk Analytics
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Supported by a Scalable Data Pipeline & a Cloud-Based, High-Performance Database for Analytics
• Bi-Directional Data Orchestration
w. 3rd Party Systems
• Integration of New Data Sources
• Near Real-time Data Streaming
• Batch Processing of Data
• Data Cleansing & Standardization
• Data Security (Decryption & Encryption)
• Data Aggregation & De-normalization
• Scheduling & Automation
Amazon
RDS and Redshift
• Canadian Consumer Data
• Member Data
• Application Data
• Credit Bureau Data
• Offers & Offer History
• Loan History
• Payment, Collections Data
• Clickstream Data
• Social Data
• Fraud Data
• Prepaid Card Txn Data
• Etc..
• Sustain Data Volume Growth
• Handle Continuously Changing
Data Landscape
• Minimal Maintenance
• Scalable, High Performance

18. 18

19. Credit Performance Results
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20. FPD - First Payment Default
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• MogoZip FPD 46% lower since peak in 2Q2013
Q113 Q213 Q313 Q413 Q114 Q214 Q314 Q414 Q115 Q215 Q315

21. Cohort Charge-Off
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• MogoZip Charge-Off of 2H2014 cohort 20% lower after 13 months performance
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
2013FY 2014h1 2014h2 2015h1