How AI and adtech are fighting India’s festive ad fraud

This week and last, marketers were busy lighting up dashboards, not diyas. Clicks flared, impressions soared, and programmatic pipelines hummed with Diwali energy. Behind all that glow, though, lurked a familiar problem: the bots were back. From fake CTV bundles to click farms disguised as influencers, India’s biggest ad season once again doubled as its biggest traffic scam.

But this time, the industry isn’t just watching the lights flicker. After years of reactive firefighting, adtech firms, agencies, and platforms are finally mounting a coordinated counterattack. The answer, fittingly, is more machine intelligence with layered verification, real-time anomaly detection, and AI systems that learn to identify ad fraud before it happens.

Before we get to the algorithms, though, it’s worth remembering that the first line of defence is still human. Sunitha Natarajan, Director of Digital Strategy at Social Panga, says the patterns are unmistakable: “There is a clear uptick on spammy sites, made-for-ads pages, and shady CTV bundles every festive season. You can see it in strange midnight surges, identical CTRs across very different creatives, and ‘installs’ that never become real users.”

Her description of the festive ad rush is apt and visual. “The way it is handled is like the 1000-wala Diwali cracker — platforms and DSPs line them up in a sequence and light it. They don’t blast the same chakri at you 20 times (frequency caps) and they keep haggling as prices rise (real-time bid tweaks). With that said, tools are good but they react too. So, for any fraud detection, the mantra lies in sharp media planning and execution. Do quality checks like how you identify real vs knock-off phuljhadis. The festival rush brings lots of new, noisy stalls, so a few fakes look real at first. So, ensure to only work with trusted partners during this time.”

Her analogy lands squarely on the problem, which is that technology can’t fix poor hygiene. But once the basics are in place, technology and Artificial Intelligence(s) can scale protection far beyond what humans can manually verify.

Nikhil Kumar, Chief Growth Officer at mediasmart by Affle, believes that’s exactly where the shift is happening. “Festive seasons bring heightened consumer demand and increased competition for digital ad inventory, which unfortunately also raises the risk of invalid traffic and ad fraud,” he said. “Modern programmatic platforms employ multiple layers of detection and prevention to ensure traffic quality and measurement accuracy.”

Kumar detailed those layers with engineer-like precision: pre-bid and post-bid filters such as country, OS, version, and referrer mismatch detection; proxy checks; duplicate-click suppression; and bid-request consistency verification. “Unique, one-time impression and click URLs, latency analysis, and time-to-click measurements further enhance precision,” he said.

As Connected TV grows into a prime festive channel, Affle has expanded its guardrails there too. “For CTV, additional safeguards like AI-driven detection (AI CTVSafe), Smart IVT filters, contextual verification, IP monitoring for SSAI manipulation, and supply vetting help maintain transparency,” Kumar explained. “Combined with real-time viewability reporting, granular session-level insights, and publisher enforcement tools, these mechanisms systematically detect emerging fraud and protect advertisers even under peak seasonal demand.”

That sophistication represents a clear leap from the reactive systems of a few years ago. Where older DSPs relied on rule-based blocking, today’s systems depend on behavioural modelling and contextual inference, and the same principles used by fraudsters, now turned against them.

Tarun Wig, Co-founder and CEO of Innefu Labs, argues that this evolution has reframed the entire conversation. “Click farms, spoofed domains, and automated traffic engines become more active when ad budgets surge,” he said. “While some brands still absorb these hidden losses, there’s growing recognition that ad fraud is not just a marketing inefficiency but a cybersecurity concern.”

“Programmatic platforms are built to scale, but seasonal surges stress both infrastructure and oversight,” he said. “The sheer velocity of bids and impressions during Diwali makes manual or rule-based checks less effective. Modern fraud-mitigation tools powered by machine learning, behavioural baselining, and device fingerprinting have improved significantly, but they still depend on timely data sharing between publishers, DSPs, and verification vendors. Continuous learning models and transparent supply chains are the way forward.”

It’s that need for data collaboration (and not just detection) that defines the next phase of anti-fraud evolution. Fraudsters don’t respect supply paths or SSP silos, so neither can the systems trying to stop them. Wig called it “the move from keeping up with fraudsters to staying one step ahead through real-time intelligence.”

Ankush Sabharwal, Founder and CEO of CoRover, has seen that shift firsthand. “We’ve noticed an increase in ad fraud during the festive season, mainly due to higher online traffic,” he said. “More brands are now budgeting for tools to detect and prevent fraud rather than just absorbing the losses.” His team works closely with platforms to implement algorithmic countermeasures.

“Programmatic platforms and DSPs are designed to handle the higher volume of bids and impressions during Diwali by using smart algorithms to optimize campaigns. While the increased traffic can make fraud harder to spot, the tools today, powered by AI, are sophisticated enough to detect suspicious behaviour,” he says.

Sabharwal added that some of the most effective interventions are platform-side. “Platforms are actively banning irrelevant ads from the ads manager, which helps to ensure only quality ads are shown to users,” he said. The strategy is simple: stop feeding the beast.

What unites all these perspectives is a shift from viewing ad fraud as a statistical anomaly to treating it as an engineering challenge. India’s advertising infrastructure (once held together by spreadsheets, screenshots, and blind trust) is now slowly adopting the logic of cybersecurity: verification, redundancy, and active defence.

The technology, though, is only as strong as the discipline behind it. Natarajan’s “phuljhadis” metaphor rings through every conversation: flashy tools without grounded planning still risk blowing up in your face. AI can light the fuse, but someone still has to aim it.

As budgets grow and festive cycles shorten, predictive prevention is quickly becoming table stakes. Behavioural analytics, latency patterning, and hardware-software fingerprinting are no longer exotic; they’re expected. The industry’s larger challenge now is transparency: convincing every node in the ad supply chain to share enough data, fast enough, for AI systems to learn and act.

If the first act of India’s festive advertising story was the Bot Bazaar, the second is the building of its firewalls. Machine learning can’t make greed disappear, but it can make it visible. And in a market where impressions once outpaced intelligence, that visibility might finally be worth celebrating.