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Machine learning for fraud prevention keeps TrafficGuard agile Posted on : Jun 20 - 2020

TrafficGuard uses machine learning to prevent ad fraud but has faced the challenges that come along with it. Full-scale commitment and investment have eased those obstacles.

In the fraud detection field, machine learning has become a necessary tool for organizations seeking an advantage. Though applying machine learning technology reads like a simple statement, mastering it requires strong companywide commitment and proper AI frameworks.

One digital ad fraud detection company, TrafficGuard, has embraced the possibilities of machine learning.

Raigon Jolly, head of analytics and data science at TrafficGuard, and his team are working with real-time fraud detection and reporting across cloud environments to reduce friction between data and actionable insights on a large scale, but their transition to machine learning use was not without obstacles.

Organizational buy-in

In fraud detection, balancing innovation across machine learning implementations, while maintaining your company's existing machine learning and data structures, can be difficult. In order to overcome challenges, there must be big organizational buy-in.

Spending a great deal of time building an organizational culture that supports and drives machine learning initiatives involves laying the groundwork of communication and culture and working hard to keep all teams pointing in the right direction throughout development and implementation of machine learning.

"[Building a supportive culture] is critical to ensure we have the input, insight and buy-in from different teams and stakeholders across the business with varying levels of technical understanding," Jolly said.

For many companies, the process of adopting machine learning has to be incremental. TrafficGuard launched with a strong machine learning component and had to build slowly and steadily as the environment changed and time advanced.

The challenge of collaboration and knowledge transfer across teams when working on rapid development cycles was difficult, Jolly said. But building an organizational culture goes a long way in overcoming this challenge. If everyone takes machine learning into consideration from the outset of a new development, then the process can be simplified.

"We have an extensive set of artifacts of this culture where data assets, schema, lineage, data flow and process flow are defined and continuously maintained," Jolly said.

"These ensure that, as new incremental developments in AI and machine learning are made, there are standards to guide them and consistency and control as we build on our capabilities." View More