Real-Time Predictive Analytics and On-Demand Decision-MakingITA Tech Talk, August 2, 2016

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What Will We Cover Today?1. Enova overview: a history of technology and analytics2. How to build a real-time predictive analytics and decision

platform3. How to effectively use functional programming to solve

business problems4. Analytics in action

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Meet Our Presenters

Joe DeCosmoChief Analytics Officer

Enova International

@jmdecosmo

Sean NaismithHead of Analytics Services

Enova International

@SeanNaismith

Vinod CheriyanSenior Data ScientistEnova International

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Growing Global Online Lender Operating 11 Brands in 6 Countries

k

• Founded and headquartered in Chicago since 2004• 1,100+ employees (500 corporate employees)• Enova Decisions® real-time AaaS with scalable,

flexible Colossus™ technology platform

• Proprietary analytics and data• Publicly traded on NYSE (ENVA) since Nov. 13, 2014

Over $17 billion in credit extended to 4MM+ customers around the world

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Advanced Analytics is at the Center of Our Customer Experience

1. Apply 2. Underwrite

Easy to CompleteIdentity,

employment, income, payroll date,

bank account information

Multi-Stage Screening

Verifies identity and prevents fraud

DecisioningIn seconds, analytics

system pulls data and determines creditworthiness

Advanced Analytics

Massive parallel processing of thousands of variables and hundreds of

algorithms; 12 years of data and millions

of transactions

3. Accept & Fund

4. Service

AcceptAgreements

reviewed and digitally signed

online

FundingACH funding by next business day in U.S.; within 10 minutes to

debit card in U.K.

Multi-Channel Service

U.S.-based in-house service center for

assistance and payment

Proprietary Systems

Tailored CRM system integrated with analytics engine and marketing

channels

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Our Team of 50+ Analytics Experts

Centralized organization

51 employees in 6 teams with 24 new hires in the last 18 months, 8 interns

Mix of college hires and experienced analysts with a mix of expertise/disciplines

2015 Excellence in Analytics Award FinalistCEO

CAO

Data Services

Business Intelligence

Portfolio Analytics Fraud Marketing

AnalyticsResearch

and Platforms

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Enova Values

Listen to customers’ needs

Provide options for hardworking customers

Deliver beyond customer expectations

Customer FirstChallenge assumptions, add unique perspectives and create the best solution

Foster innovation

Loudness and rank lose

Best Answer Wins

Think big and move fast

Roll up your sleeves

Use resources like they’re your own

Set high expectation and make it happen

Always add value

Use data to drive resultsHire and develop the best

Work in small, focused teams

Encourage diversity of thought

Operate as an Owner

Accountable for Results

We’re all analysts at Enova!

Top Talent and Teamwork

How to Build a Real-Time Predictive Analytics

and Decision Platform

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We Moved Quickly with a Phased Approach

• Initial requirements

• Vendor listQ3

2013

• RFP• Paid pilot• Final

selection

Q4 2013 • Design

and build

Q1 2014

• Complete build

• Implementation

• Training

Q2 2014 • GO

LIVE!!! Q3 2014

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Background: Home-grown System Became Limiting

MEF worked, but…it was a black box with limited experienced users!

Pros Cons[ ] Tightly integrated into production system

[ ] Developed and maintained in house

[ ] Tightly integrated into production system

[ ] Limited modeling techniques

[ ] Slow model deployment, up to 6 weeks

[ ] Tedious reconciliation, up to 2 weeks

MEF (Mathematical Equations Framework)Written in C and used proprietary domain-specific language to specify models

MEF “Black Box”

Variables

Answers

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Colossus™ Real-Time Analytics EngineIn production since early 2014, Colossus™ was built using a best-in-class scientific computing platform for customizable scoring and decisioning, allowing for the delivery of real-time analytics at scale.

[ ] Runs a variety of algorithms from regression to machine learning, as well as simple decision rules

[ ] Sub-second decisioning time

[ ] Deploys models built in SAS®, R, Python™, and other analytics platforms and environments

[ ] Integrated with multiple third party data providers

[ ] Rapid model implementation and improvement

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Enova Decisions Architecture

DECI

SION

ING

REPO

RTIN

G

Performance Dashboard

Service

Model Monitoring

Service

DatabaseTokenized, Non-PII customer data & response/request

data

DatabaseModel run data

DatabaseEnterprise

Information Store

Client views metrics thru reporting interface

Authorization &

Authentication Service

Third-Party Report Fetcher Service

Colossus™

Platform

Enova Decisions Gateway

Enova Decisions

Management Service

Colossus Gateway

APIClient application

decision

request

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Colossus Performance Snapshot

Count of Evaluations per Day

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Where is Colossus Now?

[ ] All Enova brands now in production

[ ] Cut average model deployment time by more than half

[ ] Continually improving the variable fetching and storage to drive response time even lower

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Quick PollWhere does your company get its analytics power and technical infrastructure? a. Built in-houseb. Purchased or outsourced from an external providerc. Combination of both

How to Effectively Use Functional Programming

to Solve Business Problems

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Results based on Testing Two Implementations

Business ProblemNeeded to parse and store credit reports provided as XMLs into a relational database

ChallengeMetadata is not available as schema definitions (DSD or XSD)/time-consuming to

manually translate

Solution Merging many different XMLs can provide an approximation of the complete XML for a

faster run time

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Arrived at Solution by Testing Two Analytics Approaches

Procedural Functional

[ ] Long but readable function

[ ] Poor timing

[ ] Cleaner main function

[ ] Optimal timing

0.3026419SECONDS

0.0062400SECONDS

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Biggest Insights and Advice?

1. Iterate, iterate, iterate2. Let business problems drive

the analytics approach

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Quick PollWhat do you see as your biggest challenge to adopting advanced real-time analytics at your organization?a. Organizational politicsb. Lack of business understanding or supportc. Lack of analytics and technology expertise on staff

Analytics in ActionImpact on Business Innovation and Improving the Customer Experience

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Optimize the Customer Experience at Every Decision Point

Predictive AnalyticsEmbedded in

All We Do

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Improve Customer Acquisition through Multiple Touch Points

Expected Lifetime Value to Drive Acquisitions

Credit RiskWill the customer

pay us back?

Marketing Offers

How much will we offer, for how long, and at what price?

ConversionsHow likely will the

customer take the offer?

OperationsCan we optimize payments and

customer service?

RetentionHow likely will the customer return?

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Optimizing Loan Offers

Traditional BanksDo not make offers to

a large segment of consumers

Decreases Conversions=

Enova DecisionsHelps Enova’s brands make

the right offers to the right consumers by considering more than one consumer

data source

Maximizes profitability by reducing defaults and

underwriting costs while increasing conversions

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=Alternative Lenders

Accept more consumers but offer everyone the same

product and priceIncreases Defaults

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Payment Operations

Improving the customer experience

Increasing profitability

[ ] Minimize ACH returns

[ ] Saved customers from overdraft fees by canceling payments that would be likely to return due to insufficient funds

[ ] Provide a better customer experience

[ ] Maximize the success of debit attempts

[ ] Help mitigate or minimize overall return rates and fees

[ ] Increase profitability and operational efficiency

[ ] Minimize compliance risk by maintaining high clearance rates

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Verification and Fraud

“THE FIRST TEST WAS FALSE-POSITIVE, THE SECOND TESTWAS FALSE-NEGATIVE. WHAT ARE YOU TRYING TO PULL?”

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Fraud Verification Case Study

BackgroundEntering Brazil, a new and unknown lending landscape, required

modifications to fraud practices and verification decisions

GoalUse account velocity tools to avoid fraud and optimize the

customer experience without impacting the bottom-line

Simplic saves $1M annually using fraud verification analytics

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Fraud Verification Case Study

$1M Saved by Simplic® in 2016 by Avoiding Fraud

98% Simplic Customers

2% Fraudulent Applicants per Year = $1M in Losses

Account VelocityFraud is identified when there is a velocity spike for a characteristic that’s typically low volume in a portfolio such as a location, application device, or number of inbound calls with the same phone number.

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Quick PollIn what area does your business have the biggest opportunity for improving the customer experience?a. Fraudb. Operationsc. Marketingd. Other