mFilterIt is a global Fraud Detection & Prevention Company, providing neutral, platform agnostic fraud detection solutions for brand and performance marketing.
Founded in 2015, mfilterit is a global company providing end-to-end solutions to Mobile Apps and Web Advertisers, Handset OEMs, Telecom Operators, and Ad-networks.
mFilterIt is currently the No. 1 fraud detection company in India & SE-Asia and expanding its services across the globe.
The iGaming industry is reaching millions of people around the world and earning more market value by the second.
mfilterit’s proprietary machine-learning algorithms which identify various kinds of frauds, detects BOTS and duplicate users along with a dedicated team of data scientists that track multi-layered fraud categories, thus helping its customers plug leakages in marketing spends and increase brand reputation.
mFilterIt helps advertisers get a true picture on their traffic and helps them in taking critical decisions related to their media spends such as identifying the appropriate source for a campaign.
Ad fraud has become persuasive across app and web performance marketing ecosystem. Fraudsters are targeting advertisers to receive higher payouts from them.
The tactics fraudsters employ can be complex, the basic mechanics of ad fraud are simple regardless of the environment. Fraudsters fake installs, clicks, leads, views, etc., on the basis of payout mechanism.
Fraudsters will increasingly use combined spoofing tactics to thwart detection services in the ad tech space, including spoofing geo, mobile device ID and bundle id for mobile devices. In the web space, device and cookie spoofing are the major threats. Fraud needs to be reduced from the ecosystem. Advertisers end up losing 25-30% of their revenues by paying for fraudulent customer acquisitions such as app installs, registrations, sales leads, page visits, fake activations, etc. It is important to have effective fraud detection that also ensures that user experience is not impacted.
App’s have become the default interface for users to interact digitally with people, services and platforms. Globally, there are estimated 3 million apps available on Google Play Store.
An app can be pirated. Techniques like decompiling of an app and then modifying the package with malicious code lines make an app vulnerable.
Lets briefly look at the three main threats in a pirated app.
Ad Fraud
Compromised apps are the best medium for fraudsters to control a Smartphone, a publishing medium to fake traffic, users or events. With malicious code lines put along with the app, digital ad-fraudsters get impressions, install apps as well as even trigger clicks, etc., to fake KPIs agreed with an advertiser whose campaigns are being run. At the same time, ill-practiced publishers steal the organic traffic of mobile apps to credit any activity a user does with the motive of earning the attribution without doing any hard work. This demotivates the digital marketing team as organic traffic earned after painstaking efforts is tagged as inorganic.
Brand Safety
An important ramification of a pirated app version is damage to the image and reputation of a brand. Since the app is compromised, its behaviour cannot be guaranteed to be in-line with the brand, its philosophy and guidelines. The brand through this rogue app could be seen as promoting theft of data, infringing into privacy. Since, this app is not in the control of the actual brand, it would not act as a responsible digital asset representing it.
App Piracy
Piracy has not been getting its due mindshare from the ecosystem including governments. There is a need to have strict regulatory guidelines about app piracy for the various damages it could result in. Marketers, in every organisation, must keep a vigil on the pirated versions of their apps available either over the play store or through non-play store platforms.
mFilterIt helps its clients monitor any pirated version created over several alternate app stores and also identifies the modification – addition or deletion of permissions fiddled with such duplicated versions.