Why AI is the future of fraud detection

The accelerated growth in ecommerce and online marketplaces has led to a surge in fraudulent behavior online perpetrated by bots and bad actors alike. A strategic and effective approach to online fraud detection will be needed in order to tackle increasingly sophisticated threats to online retailers.

These market shifts come at a time of significant regulatory change. Across the globe, new legislation is coming into force that alters the balance of responsibility in fraud prevention between users, brands, and the platforms that promote them digitally. For example, the EU Digital Services Act and US Shop Safe Act will require online platforms to take greater responsibility for the content on their websites, a responsibility that was traditionally the domain of brands and users to monitor and report.

Can AI find what’s hiding in your data?

In the search for security vulnerabilities, behavioral analytics software provider Pasabi has seen a sharp rise in interest in its AI analytics platform for online fraud detection, with a number of key wins including the online reviews platform, Trustpilot. Pasabi maintains its AI models based on anonymised sets of data collected from multiple sources.

Using bespoke models and algorithms, as well as some open source and commercial technology such as TensorFlow and Neo4j, Pasabi’s platform is proving itself to be advantageous in the detection of patterns in both text and visual data. Customer data is provided to Pasabi by its customers for the purposes of analysis to identify a range of illegal activities – – illegal content, scams, and counterfeits, for example – – upon which the customer can then act.

Chris Downie, Pasabi CEO says, “Pasabi’s technology uses AI-driven, behavioral analytics to identify bad actors across a range of online infringements including counterfeit products, grey market goods, fake reviews, and illegal content. By looking for common behavioral patterns across our customers’ data and cross-referencing this with external data that we collect about the reputation of the sources (individuals and companies), the software is perfectly positioned to help online platforms, marketplaces, and brands tackle these threats.”

The proof is in the data

Pasabi shared with VB that their platform is built entirely in-house, with some external services to enhance their data such as translation services. Pasabi’s combination of customer (behavioral) and external (reputational) data is what they say allows them to highlight the biggest threats to their customers.

In the Q&A, Pasabi told VentureBeat that their platform performs analysis on hundreds of data points, which are provided by customers and then combined with Pasabi’s own data collected from external sources. Offenders are then identified at scale, revealing patterns of behavior in the data and potentially uncovering networks working together to mislead consumers.

Anoop Joshi, senior director of legal at Trustpilot said, “Pasabi’s technology finds connections between individuals and businesses, highlighting suspicious behavior and content. For example, in the case of Trustpilot, this can help to detect when individuals are working together to write and sell fake reviews. The technology highlights the most prolific offenders, and enables us to use our investigation and enforcement resources more efficiently and effectively to maintain the integrity of the platform.”

Relevant data is held on Google Cloud services, using logical tenant separation and VPCs. Data is stored securely using encryption in transit and encryption at rest.  Data is stored only for as long as strictly necessary and solely for the purpose of identifying suspicious behavior.