Key to effective ad fraud prevention: AI tools have developed sharper antibodies - Part 2

Ad fraud remains a persistent and growing challenge, costing advertisers billions of dollars annually. As fraud tactics grow increasingly sophisticated, leveraging AI-powered tools has become essential to stay one step ahead. The second part of this feature story delves into the latest advancements in AI technology that are proving most effective in combating ad fraud, from real-time anomaly detection to predictive analytics and machine learning. Adgully explores how these tools identify fraudulent patterns, adapt to evolving schemes, and safeguard ad investments, ensuring greater transparency, efficiency, and trust within the digital ecosystem.

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AI tools have multiple advanced capabilities to combat fraud, but the most effective ones include anomaly detection, behavioural analysis, and predictive analytics, says Aakash Goplani, Account Director, SoCheers. These tools can analyse large datasets, model user behaviour, and predict potential fraud risks to enhance fraud prevention efforts. Investing in regular updates in these models is crucial and the only rational way to stay ahead of multiple fraud tactics, says Goplani.

Recent AI advancements in ad fraud detection focus on real-time pattern recognition and anomaly detection, notes Russhabh R Thakkar, Founder and CEO, Frodoh World. These systems, he adds, analyse user behaviour, traffic patterns, and engagement metrics to identify suspicious activity instantly.

“To stay ahead, AI models are now incorporating federated learning techniques. This allows the models to learn from diverse data sets across multiple organisations without compromising data privacy. It enables the AI to recognize new fraud patterns quickly as they emerge in different parts of the advertising ecosystem. Another key development is the use of explainable AI in fraud detection. This helps analysts understand why the AI flagged certain activities as fraudulent, allowing for quicker verification and reducing false positives,” says Thakkar.

Shan Jain, independent director, brand strategist and marketing transformation advisor, reckons that AI tools have developed sharper “antibodies” that can spot subtle differences between human users and bots. “But to stay ahead, it is a combo of “real-time learning”, wherein AI systems learn from evolving fraud tactics, constantly adjusting their algorithms to stay resilient, and “predictive precaution”, wherein by anticipating fraud patterns, AI acts like a vaccine, preventing attacks before they infiltrate the system,” adds Jain.

One of the most effective advancements is machine learning models that can look at huge amounts of data in real-time, says Vishal Rupani, Co-founder, Sprect.com.

“They can catch tiny patterns that a human might overlook, such as subtle click fraud or impression fraud. Traditionally, ad fraud detection relied on fixed rules, but now AI systems are self-learning. They use neural networks to predict unusual behaviour and advanced algorithms to identify potential fraud before it even occurs. This shift allows for quicker and more accurate responses to evolving fraud tactics. To keep ahead of fraudsters, players in the ad ecosystem need to work together and share valuable insights. Unfortunately, the reality is that many companies recognize the presence of fraudulent traffic but choose to ignore it because it impacts their profits,” says Rupani.

How to build trust?

As AI continues to play a critical role in combating ad fraud, fostering trust between AI developers and advertisers is essential for its effective adoption. Transparency is key. Developers should provide clear documentation on AI algorithms, including data sources, methodologies, and decision-making processes, along with regular performance reports on detection rates and any algorithm adjustments. Third-party audits help advertisers understand the reliability of AI tools, aligning both parties in their efforts to fight fraud effectively.

Aakash Goplani reckons that in order to build trust in AI for ad fraud prevention, developers should provide clear documentation that outlines how AI algorithms work, including their data sources, methodologies and decision-making processes, followed by regular performance reports including detection rates, false positives, and adjustments made to algorithms.

“This transparency helps advertisers understand the reliability of the AI tools. Additionally, third-party audits can enhance transparency. By implementing such measures, AI developers and advertisers can foster a collaborative environment that enhances trust, ensuring that both parties are aligned in their efforts to combat ad fraud effectively,” says Goplani.

One effect is observed in education and another in awareness, points out Kruthika Ravindran, Director, Key Accounts, TheSmallBigIdea. She suggests that advertisers should be provided with resources that will explain the advantages and disadvantages of using AI in combating ad fraud. “This would enable advertisers to make well-informed choices when adopting AI tools. Additionally, a feedback mechanism is essential, allowing advertisers to provide input on the AI tools they use and suggest improvements. In turn, AI developers can refine and enhance their tools based on this feedback, creating a continuous cycle of improvement,” she adds.

Russhabh R Thakkar is of the opinion that building trust in AI’s role in ad fraud prevention requires a multi-faceted approach centred on transparency, education, and standardization.

According to him, transparency involves clearly communicating how AI systems make decisions, their capabilities, and their limitations. This doesn’t mean revealing proprietary algorithms, but rather providing meaningful insights into the AI’s decision-making process.

Thakkar stresses that education is crucial; regular training and workshops for advertisers and other stakeholders can demystify AI and its application in fraud prevention. This knowledge, according to him, empowers users to make informed decisions and use AI tools more effectively.

“Standardization efforts, such as developing industry-wide benchmarks for AI performance in fraud detection, are essential. These standards provide a common framework for evaluating different AI solutions and build confidence in their effectiveness. Lastly, independent audits of AI systems can provide unbiased verification of their performance, fairness, and compliance with privacy regulations, further enhancing trust in these technologies,” he concludes.

According to Vishal Rupani, one way to foster trust is for AI developers to provide advertisers with clear explanations of how their algorithms work and how they are used to detect fraud without revealing their secret sauce.

He feels that this can help advertisers understand the limitations of AI and avoid overreliance on these technologies.

Additionally, he adds, AI developers should be transparent about the data that they use to train their models and how they protect advertisers’ privacy. By being open and honest about these issues, AI developers can build trust with advertisers and ensure that AI is used effectively to combat ad fraud.

According to Shan Jain, trust-building is a function of three things:

Transparency in function: Wherein advertisers need clear insight into how AI operates, much like understanding how a vaccine works gives us confidence in its protection.

Open dialogue: Developers and advertisers must communicate like doctors and patients, sharing data and feedback to optimize the AI's 'immune response' to fraud, creating a unified defence.

Ethical guardrails: Just like an immune system should target the right threats, ethical AI ensures it fights fraud without overreaching, causing collateral damage, or compromising user privacy, balancing effectiveness with responsibility.

“Trust in AI is like trust in our immune system – you just need to know it’s working and working on the right problems," concludes Shan Jain.