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ANALYTICS: WHAT IS IT REALLY, AND HOW

CAN IT HELP MY ORGANIZATION?

A FINTECH SUCCESS STORY ON ANALYTICS

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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

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ANALYTICS

Analytics solve business problems by exploring

an idea with data.

Are no longer a science experiment.

- Steve Holder

an·a·lyt·icsˌanəˈlidiks/ noun

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Exploration

Data Visualization

ANALYTICS WHAT DOES ANALYTICS MEAN TO YOU?

Traditional ReportingCreation and sharing of known

information resulting in the delivery of

reports

Data DiscoveryExploration of data to

discovery new questions and

opportunities

Advanced AnalyticsLeverage Data to make the best

decision

Forecasting

Optimization

Prediction

Standard Reports

Dashboards

Ad Hoc Query

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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

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ANALYTICALLYIMMATURE

ANALYTICALLY AWARE

ANALYTICALLY

INFORMED

ANALYTICALLYRELIANT

ANALYTICALLYINNOVATIVE

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

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SAS Webinar

December 8th, 2015

Underwriting Process

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Mogo Credit Programs

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MogoLiquid MogoMini MogoZipBetter 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 paydayloans

Peggys Cove, NS

Predictive Risk Analytics is provingcritical for online lending:

Mogo hired a credit risk teamand leveraged risk analyticsfor credit decision and KYC

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

Analytics on Mogo Credit Process

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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

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 predictivemodels 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

Example of Predictive Modelingbased on Risk Analytics

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16

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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

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Credit Performance Results

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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

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