a cure for ad-fraud: turning fraud detection into fraud prevention

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A CURE FOR AD FRAUD TURNING FRAUD DETECTION INTO FRAUD PREVENTION

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Page 1: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

A CURE FOR AD FRAUDTURNING FRAUD DETECTION INTO FRAUD PREVENTION

Page 2: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

INTRODUCING THE SPEAKERS

RAYMUND BAUTISTA

Head of Strategic Partnerships

linkedin.com/in/raymundb @therealraymund

GRANT SIMMONS

Director of Client Analytics

linkedin.com/in/grantsimmons

Page 3: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

AD FRAUD:

WHO IS TO BLAME?

EVERYONE!

Page 4: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

AD FRAUD IS A MAJOR PROBLEM

GLOBAL LOSSES

DUE TO AD FRAUD

$16.4 BN

OF ALL DIGITAL AD SPEND

IS SUSPICIOUS IN THE US

10%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

Smartphone Fraud Impression % by Country

Page 5: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

ADVERTISERS AND NETWORKS ARE

SUSCEPTIBLE TO FRAUD IN TWO WAYS

1

Install-based fraud where the clicks, installs

and users are all non-existent

5

2

Misattribution where installs are valid but

credit is stolen from clean networks

Page 6: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

TYPES OF MOBILE AD FRAUD

Automated Traffic

Unauthorized Re-brokering

Click Spamming

Ad Stacking

Accidental Clicks

Click Sniping

1

2 5

4

3 6

Page 7: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

DIAGNOSTICS

& ANALYSIS

DELIVERING ACCOUNTABILITY

What can networks do to provide fraud-free traffic?

MEASUREMENT

& TRANSPARENCY

PUBLISHER QUALITY

& CONTROL

Page 8: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

Publisher Onboarding Checks

Content Quality

Publisher properties

undergo thorough

evaluation and

ownership verificationBrand Safety

Delist brand unsafe, hosting malware

or not sending app bundle ID or

mobile website domain.Delisting Duplicates

Publishers are prevented from creating duplicate

accounts once blacklisted.

Arresting Site Subletting

Extensive technical checks ensure ads are

rendered on the registered and verified

publisher property.

Checking Request Patterns

Devices are monitored for unusually

high request volumes.

Page 9: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

Diagnostics & AnalysisRuntime checks

Cryptographic

Signatures

Cryptographically

secured clean

impression-to-click-to-

install mapping Discarding Automated Traffic

Identifying bots and scripts through

pattern analysis of impressions and

clicks in real time.

Velocity Checks

Velocity checks to prevent ad

fatigue and to ensure that every ad

unit has a fair chance of being

registered by the user.

Double-Checks on Data Signals

Publisher data signals authenticated against

data collected directly by the InMobi SDK to

discard any invalid data.

Studying Suspicious Activity

Integrations with leading measurement and

attribution platforms to identify and analyze

all suspicious behavior.

Page 10: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

Measurement & TransparencyThird-party checks

Audience Verification

Audience verification tags

providing external

verification on

demographic data

segments. Viewable Inventory

Integrations with viewability

measurement providers to

certify viewable

impressions across all

campaigns.Tracking Quality of

Installs

Extensive partnerships with

third-party providers to

analyze quality of installs

and complement internal

identification of invalid

data.

Straining Invalid

Traffic

Clicks and renders are

actively screened for

suspicious patterns

Page 11: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

HOW CAN WE FIGHT FRAUD?

INVEST WISELY

Work with networks and partners

that are heavily invested in fraud

prevention tools.

DEMAND TRANSPARENCY

Demand transparency into

campaign data for performance

safety and campaign cleanliness.

METRICS THAT

MATTERInvest in quality traffic that is

certified by industry bodies; partner

with leading measurement

platforms.

DEFINE STANDARDS

Agree to standards and

terminology. Go beyond the install.

Shift to an “optimum acquisition

cost’” model.

Page 12: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

Fraud Highlights

Page 13: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

OVERVIEW

What is “normal?” Looking at the past 90 days: Average network: 15.4% of clicks are fraudulent, 4.1% of installs

Breakdown:

• The Top 10 highest volume networks generate 84% of all Fraudulent clicks

• The Kochava Blacklist is able to identify that 27% of the Top 10 network’s installs are fraudulent

• There are specific networks whose total clicks exceed over 50% of blacklisted traffic

• There are dramatic differences by network by platform. A particular network on Android, has 45% of it’s clicks identified as fraudulent, but less than 1% on iOS.

Page 14: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

CLICK SPAMMING, CLICK INJECTION

ATTRIBUTION FRAUD

UNREASONABLE CTI RATES AD STACKING

TTI OUTLIERS

Page 15: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

CLICK SPAMMING, CLICK INJECTIONATTRIBUTION FRAUD

IPs WITH HIGH CLICK VOLUMECLICK-TO-INSTALL TIME DISTRIBUTION

Page 16: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

MANUFACTURED INSTALLS OR TRAFFIC

DEVICES WITH HIGH CLICK VOLUMEMTTI

ANONYMOUS INSTALLS

Page 17: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

DETECTION & MITIGATIONDetection – Fraud Console

Mitigation – Blacklist Curation

• Fraud Reporting Console:

• Reporting specific to clients' accounts and apps

• Visibility to statistical outliers

• Suspicious activity worth investigation

• Fraud Blacklist

• Observed behavior across accounts and apps

• Higher thresholds:

• Must be observed across minimum number of

apps, min number of installs to be flagged

• More stringent: additional standard deviations beyond

the fraud console

• Advertiser can monitor only, or not attribute

• Advertisers have the ability to curate/add to their own

blacklist

Page 18: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

WHY ALL THIS FRAUD TO BEGIN WITH?

• Attribution Fraud: 70% of what we detect

• Theory:

• DR attribution demands instant feedback loops

• Rewards last click, not maximized reach

• So, networks are incentivized to be the last click

• UA function, though, is to maximize REACH

• The first impression does the most incremental ‘work’

• Thus the goal of networks should serve as many first impressions as possible

• However, the attribution dynamics rewards the last interaction:

this results in the click spamming everyone's witnessing.

• However, there is value in collecting all of the touchpoints leading to an install

• Additional attribution frameworks:

• MTA (may not mitigate fraud, however)

• Incremental (remarkably difficult in digital, more so with mobile)

Page 19: A Cure for Ad-Fraud: Turning Fraud Detection into Fraud Prevention

QUESTIONS?