MUMBAI: Fraudsters may click fast, but AI is learning to click back even faster. Affle has secured an Indian patent for an artificial intelligence-powered system that detects fraudulent app-install activity in real time, strengthening the adtech company’s technology portfolio as advertisers battle rising bot traffic and invalid digital engagements.
The patent, titled ‘Method and System for Click-to-Install Behaviour Based Detection of Fraud’, had earlier been granted in the United States and has now received approval in India, the company said in a regulatory filing.
The AI-driven system uses trained machine learning models to analyse click-to-install behaviour across agentic, or autonomous intelligent, connected devices. By processing signals such as click time, redirection time, installation time, application data, network signals and user behaviour, the technology is designed to distinguish genuine consumer activity from fraudulent installs and automated bot traffic.
According to Affle, the system assigns fraud scores to suspicious activity and can block offending publishers in real time, helping advertisers ensure they pay only for authentic consumer conversions. The technology also strengthens fraud prevention capabilities within the company’s AI-powered Consumer Platform Stack, which supports its cost-per-converted-user (CPCU) business model.
The latest approval expands Affle’s intellectual property portfolio to 18 granted patents from 39 patent applications, representing nearly 300 enforceable patent claims across its technology stack. The company said the growing portfolio reflects its long-term investment in artificial intelligence, fraud detection and privacy-first digital advertising.
The patent comes as digital advertisers increasingly tie spending to measurable outcomes such as app installs and customer acquisition. With invalid traffic and click fraud continuing to erode campaign performance, AI-led detection systems are becoming a critical tool in safeguarding advertising budgets and improving return on investment.
