Fraud Console for Networks

Leading fraud abatement since 2015

Long before mobile advertising fraud was on the radar for brand advertisers, sophisticated performance advertisers were learning that it was a costly and challenging problem. As a measurement provider for some of the highest-volume performance advertisers, Kochava recognized abnormalities in campaign data that in some cases were fraudulent. Thus, began the fight against mobile advertising fraud, and since 2015, we’ve been developing the most astute tools to prevent, identify, and abate it.

Networks caught in the middle of mobile advertising fraud

Mobile advertising fraud affects both marketers and networks alike. While the media like to focus on the challenges advertisers face in dealing with mobile fraud, the reality is that networks often get squeezed the hardest. Because networks typically have to pay publishers for traffic before advertisers are billed, networks end up holding the bag on losses from fraud.

By the time advertisers reconcile the cost of their traffic against any fraud they detected during the campaign, networks have already paid for said traffic. Thus, networks are not only blindsided by reports of fraud but also stuck offering credit to advertisers for the fraudulent traffic they already paid for.

Fraud also eats into network profits. Out of the total ad spend, fraud diminishes the ad value for a campaign as well as a network’s credibility to advertisers. The competition for networks is already tough, as they compete with media agencies, trading desks, DSPs, ad exchanges and other service providers for market share.

Financial breakdown showing how fraud erodes ad spend value..

Break a vicious cycle

The Kochava Fraud Console for Networks provides industry-leading fraud abatement tools, allowing networks to identify fraud before the advertiser does, and detect fraudulent traffic before paying sub-publishers for it.

The visualizations show both obvious fraud and data anomalies, flagged for review. By using the Fraud Console, networks show their credibility in doing their part to uphold ethical industry standards. Preventing fraud means saving revenue and time spent negotiating amends after the campaign.

Data visualizations of statistical outliers

The console includes 11 reports that leverage statistical methodologies and pattern identification to flag all identifiable fraudulent tactics present in the ecosystem.

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“The Kochava fraud reports have been invaluable in assessing partners and have even provided the solid evidence necessary to secure significant refunds for bad traffic. Now, with Traffic Verification and the Global Fraud Blacklist, we have two great front-end tools to help avoid the issue altogether.”

Kevin Grimes, Ad Operations Manager, DoubleDown Interactive

“The Fraud Console from Kochava has been an integral part of how we optimize campaigns on our client’s behalf. Based on the fraud indicators, we can quickly shift budget away from known fraudulent publishers to ensure our clients’ dollars are being utilized to drive valid users via legitimate traffic sources.”

Nate Gasser, VP Emerging Media and Technology at Camelot Communications

“Mobile attribution player Kochava souped up its anti-fraud offering on Thursday with a consolidated suite of reports, including access to a Global Blacklist that updates itself.”

Allison Schiff, AdExchanger, March 30, 2017

High Click Volume IP Addresses
1. High Click Volume IP Addresses
High click volumes for IP addresses obscure campaign outcomes and raise a flag that fraud may be present. This abnormality can also be a leading indicator of issues with server-to-server click feeds.

High Click Volume Devices
2. High Click Volume Devices
A high volume of clicks from a single device is a strong indicator of click injections from bots or hijacked devices and are flagged for further investigation.
Graph of Mean-Time-to-Install (MTTI) Outliers
3. Mean-time-to-install (MTTI) Outliers
MTTI is the average time between the click and install and varies by app and network. When MTTI varies greatly, the data is flagged for review.
Graph of Time-to-Install (TTI) Outliers
4. Time-to-install (TTI) Outliers
Time to install (TTI) is an important indicator of install validity. The time between click and install depends on physical factors (network speed, size of app binary, etc.) and behavioral ones (incentivized installs typically happen within a short time). Determine the mean TTI for your app on a given network and sub-publisher as a baseline from which to measure. The TTI Outliers view indicates potentially fraudulent traffic based on the baseline TTI and shows abnormally fast installs.
Graph of Geographic Click / Install Delta
5. Geographic Click / Install Delta
Most installs occur within close proximity to the attributed click. Variances between the location of the click and that of the install may be indicators for fraud.
Graph of Platform Click / Install Mismatch
6. Platform Click / Install Mismatch
Ads should not be repeatedly served to the wrong platform. This is more than a mistake and may indicate the presence of bot farms generating fraudulent traffic or poorly targeted traffic.
Graph of Multi-Hash Attribution Matches
7. Multi-Hash Attribution Matches
Networks and publishers often hash device IDs to provide a level of privacy. If IDs are hashed multiple times, it may indicate rebrokered traffic. It is not fraud if done transparently, but if the traffic is exclusive, this may indicate fraudulent behavior.
Graph of Ad Stacking Clicks
8. Ad Stacking Clicks
When multiple ads were clicked at the same time from the same device, this strongly suggests click/impression stuffing or viewability fraud.
Graph of Anonymous Installs
9. Anonymous Installs Kochava blacklists sites that use anonymous proxies, VPNs and TOR exit nodes. Sites obscuring their identity create suspicion.
Graph of Non-Verified Install Receipts
10. Non-Verified Install Receipts
Kochava receives an install receipt that can be independently verified for installs originating from the iTunes Store. If unverified, it is considered fraudulent.
Graph of Non-Verified Purchase Receipts
11. Non-Verified Purchase Receipts
Kochava validates purchases for both iOS and Android against their respective store servers. Kochava surfaces unverifiable purchases as fraudulent.
Non-Verified Purchase Receipts
12. Click Flooding
This type of mobile app fraud occurs when a network or sub-publisher floods its channels with clicks until a user installs. Because of the frequency of clicks, the network/sub-publisher illegitimately receives attribution. This type of fraud is detected when clicks outnumber campaign installs. The view shows those networks and sub-publishers with an unusually high click-to-install ratio.
Non-Verified Purchase Receipts
13. TTI Distribution
For most campaigns, the majority of installs occur within the first few days of launch and then trail off as the campaign continues. The TTI Distribution view shows the number of installs that occurred within a five-day window of the click or impression and also outside of it. This visualization highlights cases where there is an increase in installs with time, which is typically considered abnormal behavior.

Fraud Abatement Series

Grant Simmons, Director of Client Analytics at Kochava, is our resident expert on detecting mobile advertising fraud. He leads his team in business value assessments and fraud audits for customers. Read through his series for an expert’s take on fraud and how it is identified.

Part 1: Detecting Fraud by Counting Clicks With advertisers often focusing on install rates, they may miss if there there is an imbalance in the click-to-install ratio where clicks may far outnumber the installs driven from a site. Read More.

Part 2: Devices With High Click Volumes and Fraudulent Traffic Bots or hijacked devices are often the culprits behind high click volumes. While some flagged data is obviously abnormal, some apps have more volume than others, so they need to establish what is their baseline normal. Read More.

Part 3: The Fraud Behind Install Metrics Mean time to install (MTTI) and time to install (TTI) show the time a user takes from the initial click to the app install. This metric varies according to the app and campaign. Abnormal patterns are flagged for further review. Differences in the two metrics indicate incentivized traffic or poorly targeted, low quality users. Read More.

Part 4: Ad Stacking One of the easier types of fraud to detect is ad stacking. This type fraud occurs when multiple ads are placed on behind a single ad placement. When a user clicks on the visible ad, it is registered for all ads in the stack. Read More.

Part 5: The Global Fraud Blacklist The Global Fraud Blacklist excludes known fraudulent entities (device IDs, site IDs, and IP addresses) from campaign traffic. These entities are repeat offenders of fraudulent activity or that have exceeded established thresholds outside of normal activity. Read More.

Kochava CEO, Charles Manning, wrote about how Kochava is handling fraud abatement, at scale.

Director of Client Analytics, Grant Simmons, wrote a whole series on Kochava fraud detection and abatement.
“With a neutral attribution partner like Kochava, it is easy to verify install claims made by ad networks to ensure that we are only paying for verified users.”

Mark Braatz, former VP of User Acquisition, KIXEYE

“The dashboard helps advertisers visualize what’s happening with their traffic in real time, with views into things like abnormally high click-to-install ratios, ad stacking clicks and questionable IP addresses.”

Allison Schiff, AdExchanger, March 30, 2017

“The amount of validation you’re able to do through Kochava makes the process of detecting fraud possible, whereas previously it was impossible.”

Kevin King, User Acquisition Director, Backflip Studios

“Essentially, we’re providing verification that an ad was displayed as it was intended,” Manning said. “There’s an enormous amount of time and energy that our customers—both marketers and networks—invest in creating or distributing a brand’s product. Digital ad fraud is a parasite that wipes that investment away, and the Fraud Console is the firewall to prevent infiltration.”

Charles Manning, CEO of Kochava

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