Digital
Click-bait and switch: AI fraud spins a new web around digital advertising
MUMBAI: Click, scroll… fooled again? In a plot twist worthy of a digital thriller, mFilterIt’s Ad Fraud Intelligence Report 2025 reveals that the world of online advertising is being quietly hijacked by a new kind of impostor: AI-shaped fraud that looks, moves, and behaves uncannily like real users, slipping past traditional defences with a confidence that would impress even the boldest scammer. What once looked like a technical hiccup now emerges as a full-blown trust crisis.
The report, based on billions of validated data points across platforms, shows the scale of the upheaval. Fraud sophistication has tripled in just two years, creating an ecosystem where even “clean” traffic can no longer be taken at face value. Programmatic campaigns, for instance, saw between 30 and 45 per cent of supposedly valid traffic fail deeper checks. Walled Gardens, long considered the industry’s gated sanctuaries, showed 9 to 18 per cent of activity with signs of behavioural manipulation, a figure that becomes even more damaging because these environments run on premium CPMs and CPCs. Meanwhile, affiliate networks remain a messier battlefield, contributing 43 per cent of the invalid traffic detected, often through lead punching, organic hijacking, duplicated events, and inorganic installs masquerading as high-intent users.
Even the old comfort of “viewability” has now become little more than a technical nicety. The report dismantles the myth that viewable impressions are genuinely seen by humans. AI-driven bots, operating across multiple channels, now mimic real browsing behaviour so convincingly that they complete scroll gestures, replicate dwell times, and interact with content at human-like intervals. The result is a flood of impressions that are technically viewable, yet deliver zero actual human attention. Ads routinely appear in environments such as MFA (Made-for-Advertising) sites that stack or stuff multiple placements, pass viewability benchmarks with ease, and still offer no meaningful exposure. Across audits, mFilterIt found numerous cases where ads achieved perfect viewability scores while human engagement was non-existent.
Brand safety, often treated as a solved problem, also emerges as a façade. Legacy systems built on keyword filtering, English-first logic, and surface-level metadata are now woefully inadequate in a digital world dominated by visuals, reels, thumbnails, regional dialects, and cultural nuance. The report documents misclassified content across YouTube, OTT, and UGC platforms, where ads meant for general audiences ended up beside gambling pages, emotionally charged vernacular videos, or unsuitable made-for-kids content. In fact, 7 to 9 per cent of YouTube impressions in analysed campaigns appeared on children’s content, a direct waste of money and a massive mismatch in targeting relevance. Visual-first formats repeatedly slipped past keyword filters, and regional languages across India and the Middle East were routinely misunderstood or entirely misread by traditional tools.
Frequency capping, another long-standing comfort blanket of advertisers, fares no better. The belief that setting a cap guarantees controlled exposure simply doesn’t hold. The report shows that 15 to 20 per cent of CTV and OTT impressions violated their assigned caps, often showing users the same ad eight to twelve times despite a supposed ceiling of three. Because platforms apply frequency as an average rather than a maximum, some users barely see ads while others are bombarded. The fragmentation of user identities across devices, spoofed IDs, and reseller delivery paths makes these violations nearly invisible. The outcome is predictable: irritated audiences, declining attention, limited reach, and skewed optimisation.
App ecosystems, once thought to be the cleanest segment of the funnel, reveal their own cracks. Attribution platforms report “clean installs”, but fail to validate whether the user behind the install is real. According to mFilterIt, between 45 and 55 per cent of installs in some campaigns displayed anomalies such as device duplication, automated install farms, spoofed sessions, or unnatural click-to-install times engineered to hijack organic users. In one case, a petroleum client discovered that 21 per cent of its “clean” installs were actually referral coupon abuse, draining budgets without adding a single meaningful user.
Affiliate and performance-driven ecosystems continue to attract sophisticated manipulation. One automobile brand found that 70 per cent of invalid events were generated by a single affiliate partner through punched leads. Across multiple campaigns, mFilterIt observed up to 35 per cent of affiliate traffic showing inorganic patterns, robotic form fills, or action-driven manipulation that made conversion metrics look exceptional, even as actual business outcomes declined. High conversion rates, often treated as a badge of campaign health, are shown to be just as vulnerable; 30 to 35 per cent of in-app events in some fintech and crypto campaigns were fraudulent despite “strong” reported CVRs.
Influencer ecosystems do not escape scrutiny either. The report reveals that follower counts and engagement rates, the industry’s favourite shorthand metrics, hide vast chasms in audience quality. Some influencers analysed had fewer than 20 per cent suspicious followers, while others crossed the astonishing threshold of 90 to 100 per cent, raising questions about inorganic growth, bot-based engagement, and artificially inflated sentiment. Without authenticity checks, brands risk paying for reach that never actually reaches anyone.
Retargeting is another quiet casualty. Since bots, spoofed devices, and incent-driven users generate actions that drop cookies or identifiers, remarketing lists become contaminated by non-human audiences. Engagement partners that fire phantom clicks often hijack organic traffic or register sessions immediately after an install. In one case from a quick-commerce platform, random background clicks attempted to claim organic conversions, distorting the entire optimisation pathway. Retargeting then becomes an exercise in chasing ghosts — audiences that look warm on paper but cannot convert because they never existed.
All this is unfolding against an expanding global digital ad market projected to reach $678.7 billion in 2025, representing 68.4 per cent of all advertising. Retail media, growing at 13.9 per cent, social at 9.2 per cent, programmatic at 8.4 per cent, and CTV/OTT at 10.9 per cent, offer abundant opportunities, and equally abundant chances for AI-led fraud to seep in unnoticed.
The report ultimately reframes the issue: this is no longer a traffic problem but a trust problem. As CEO Amit Relan puts it, “The real risk in digital advertising is not fraud itself, but the illusion of clean data.” CTO Dhiraj Gupta echoes the urgency, noting that fraud now mirrors human behaviour with such fidelity that rule-based systems stand no chance. Traditional metrics: viewability, clicks, CTR, and installs, have lost their authority. The industry’s next frontier lies in full-funnel validation, multi-signal intelligence, contextual understanding, and attention-led measurement rather than surface-level exposure.
mFilterIt calls for advertisers to move away from fragmented verification and towards systems that connect impression integrity, contextual safety, behavioural authenticity, and conversion truth. In an AI-accelerated landscape where every stage of the funnel can be distorted, digital trust is no longer a nice-to-have, it is the new measure of performance. If the advertising world once asked “Where did my ad run?”, the new question might well be “Did any of it reach a real human at all?”
Digital
GUEST COLUMN: How AI is restructuring distributor and retailer motivation models
From incentives to intelligence, AI is redefining how brands engage channel partners
MUMBAI: Artificial intelligence is rapidly transforming how brands engage with their most critical yet often overlooked stakeholders: distributors, retailers, and last-mile influencers. For Abhinav Jain, co-founder and CEO of Almonds Ai, this shift marks a fundamental departure from traditional, transaction-led incentive models toward behaviour-driven, data-intelligent ecosystems. In this piece, Jain examines how AI is enabling brands to decode partner motivations, predict engagement patterns, and deliver personalised, scalable experiences—ultimately redefining channel relationships from transactional exchanges to long-term growth partnerships.
Across many sectors, there is increasing recognition that motivating those who bring products to market (distributors, retailers, last-mile influencers) poses a growing challenge.
Brands continue to invest significant marketing and digital resources to consumers, yet in many countries and the vast majority of emerging economies, these types of consumer-focused investment areas have had little impact on ultimate product delivery. Rather, it is still the case that traditional retail continues to make up most products sold.
So why is it that the systems built around motivating these channels have yet to evolve?
For decades, distributor and retailer engagement revolved around static schemes – quarterly targets, volume-based rewards, and occasional trade promotions. These programs were designed around transactions, not behaviour. The assumption was simple: if incentives increase, performance will follow.
Now, with the advent of artificial intelligence, the definition of performance is being challenged.
With the development of artificial intelligence, businesses can move beyond simply creating loyalty based on transactional-based models and toward models built on behaviours, the behaviours of channel partners that are intrinsic to their motivations in engaging with particular brands. As a result, the means by which businesses develop relationships within their distribution network are starting to evolve; thus, ultimately changing how brands interact with those within their distribution network.
Assessing engagement: Transitioning from transactional- to behavioural intelligence
Traditional loyalty systems refer to transactional activity (sales data). Although this data is valuable and important, it only provides a partial view of engagement across the channel partner.
For example, a retailer may have a high frequency of sales of a product, but their lack of engagement with the manufacturer would not reflect that they have true loyalty toward that brand. Conversely, a retailer who actively participates in training programmes, acts as brand advocates, and is engaged in learning with the supplier would exhibit more profound levels of loyalty but would have been invisible based on historical incentive programmes.
Artificial intelligence allows for the identification of behaviours that help to address this gap. Brands are able to use a variety of engagement data points, participate in learning programs, respond to communications, redeem behaviour and track platform use behaviour in order to identify motivation through behaviour.
McKinsey has stated that companies that leverage advanced analytics for their sales and distribution functions can achieve as much as a 15-20 per cent increase in productivity due to increased awareness of their behavioural trends throughout their networks.
This visibility of behavioural patterns within channel ecosystems can be transformational to brands as they can now view how partners engage on their path to purchasing products, instead of just measuring the sales revenue generated by those purchases.
Predicting motivations, not just measuring performance
Possibly, the largest contribution of Artificial Intelligence (AI) to helping brands engage with partners via channel ecosystems is its ability to predict future engagement versus simply measuring past performance.
Traditionally, brands only realised that a partner was disengaged (not likely to purchase products) once their sales performance had already declined. By then, the brand would have to use significant amounts of incentives or aggressive promotional activities to recovery their partner’s engagement level.
AI models can help organisations to detect early signs that a partner is becoming disengaged, such as declining participation in learning modules, declining interaction via the platform, or slower reward redemption rates. These indicators can help organisations to proactively engage with their partners before their sales performance begins to decline.
The practical application of AI and predictive analytics gives brands the ability to re-engage with their partners prior to their sales performance declines. For example, instead of developing and implementing broad-reaching incentive programs that provide a “one size fits all” incentive to all partners in an ecosystem, brands are able to develop targeted, engaging re-engagement programmes. This is how personalisation can be done on a large scale, such as across global distribution and retail networks.
The vast majority of distributor and retailer channels have thousands, if not millions, of individual channel partners. Historically, providing personalisation to such a large number of businesses has not been feasible.
However, with the advent of AI, personalisation at scale is becoming a reality.
Brands can now create tailored engagement journeys for all their partners, based on their partner profiles, through some combination of machine learning models and behavioural segmentation. For example, high-performing distributors might receive higher levels of leadership-based recognition and greater incentives to continue to grow. Emerging retailers, on the other hand, might be supported with training, onboarding rewards, and measurable performance milestones.
The shift towards personalisation of partner engagement echoes the direction that consumer marketing is already moving towards.
According to Salesforce’s report, over 70 per cent of customers expect personalisation in the way that brands engage with them. As such, there is a growing expectation for B2B ecosystems to have these same types of expectations from their channel partners.
Gamification and continuous engagement
AI is also radically changing how brands will engage with their channel partners through the use of gamification.
Many traditional incentive-based contests and leaderboards would spark temporary engagement among their participants, but they struggled to sustain engagement over time. With the use of AI, gamification mechanics are evolving dynamically based on historical and evolving participation patterns by their channel partners.
Challenges, rewards, and recognition structures can be modified continuously in order to sustain engagement with all of a brand’s partner segments. This will provide a greater opportunity to move away from episodic campaigns towards ongoing, continuous engagement experiences.
When channel partners receive motivation as part of their daily business activities through recognition, learning, and tracking their performance, long-term loyalty will be achieved.
Aligning motivation to broader impact
There is a growing trend within the channel ecosystem to integrate sustainability and socially responsible behaviours into the channel partner programmes of brands.
Increasingly, brands are motivating their partners to use sustainable practices in their operations, participate in sustainable practices like sustainability-related knowledge programmes, or promote products that are in line with their sustainability objectives.
Brands can use AI to monitor and measure these types of behaviours and incorporate them into their incentive frameworks so that brands can align their commercial objectives with broader social and environmental outcomes.
A shift in the way brands view their channel partners
AI is having the most significant impact on the way that brands are now viewing their channel partners, as it relates to the underlying philosophy of those fundamental relationships.
For the past several decades, many brands have viewed their channel partners as intermediaries in the supply chain. More and more brands are now beginning to view their channel partners as key ‘partners-in-growth,’ and their actions can have a direct impact on market performance.
In fact, all the channel ecosystems are using behavioural engagement platforms to design new models that reward not just transactional behaviour, but also create continuous engagement journeys for their partners, where their partners can receive recognition for their participation, learning, and continued engagement, thereby reinforcing long-term loyalty to the brand.
The future: Intelligent channel ecosystems
As we consider what the next phase of channel engagement may look like, many believe that it will be based on intelligent ecosystems, using AI to continuously monitor and adjust the engagement strategies used to engage their channel partners, in real time and based on the behaviours of those partners.
For brands operating in complex distribution networks, the ability to perform well will be determined both by whether products are available to their customers, as well as by the enthusiasm, expertise, and loyalty shown from each channel partner that represents the brand each and every day that they are working on behalf of the brand.
While AI clearly does not eliminate the human aspect of a brand’s relationship with its channel partners, it does allow brands to better understand and nurture that relationship.
In markets where the last mile will determine whether a sale is made, how one leverages the intelligence gained by using AI will ultimately be the difference between gaining a new, sustainable competitive advantage versus losing one.
