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Writer's pictureAbhinav Bangia

Introducing 3 levels of deterministic tests assessment for intentional advertising fraud.

Updated: Apr 28, 2021


Advertising Fraud is a growing menace among advertisers globally. HP Enterprise in its Business of Hacking highlighted ad fraud as the most easiest and lucrative cybercrime. In a 2017 report Juniper Research estimates ad fraud to be worth US$19billion equivalent to $51 million per day. This figure, representing advertising on online and mobile devices, will continue to rise, reaching $44 billion by 2022. Fraud is generally defined in the law as intentional misrepresentation of information or material’s existing fact made by one or multiple people to another person with knowledge of its falsity and for inducing the other person to act, and upon which the other person can take on severe damages in terms of performance, reputation and finances.


Goals for Assessment


1. Highlight the fraud focus points (High) where the performance of an audit may need to be adjusted.

2. Provide assurance that the risk of ad fraud is being effectively incorporated within the risk assessment.

3. Minimise the risk of overlooking fraud during assessment stages.

4. Build Reports for clients admissible in court for fraudulent traffic supply.


Com Olho’s Risk Model


A risk model maps and assess the advertiser’s vulnerability to identify ad fraud scenarios, with a scale defined as below :

Fig 1.1 : Com Olho's Risk Model

Tests


1. Deterministic Deep Tech based single test to find presence of fraudulent advertising traffic.

2. Non Traditional Deterministic Test for Organic Hijacking.

3. Non Traditional Deterministic Test for Bot Mixing.


Results


Upon investigation, depending on scale of campaigns, KPI of campaigns, advertiser awareness etc. Fraud impacts all forms of advertising budgets, even with the most strongest KPI's. Follow the table below to understand vulnerabilities percentages.


Fig 1.1 : Vulnerabilities Percentages v/s KPI


Want to learn more about the tests? Drop an email to abhinav@comolho.com


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