Combating Account Fraud With Machine Learning: A Webinar with Feedzai and Aite

Financial institutions are applying machine learning to stop account takeover fraud and create an orchestration layer for the authentications systems already in place. To learn about the results these organizations are enjoying, join a webinar on Wednesday, Dec. 13th, at 12 pm EST. Feedzai and Aite will be discussing how financial institutions can realize the power of machine learning to fight fraud and improve customer experience.

Register here.

“Account takeover bounces back.” Those are the words of a Javelin report that showed a resurgence of account takeover fraud after reaching a low point in 2014. Since then, total ATO losses reached $2.3 billion, a 61% increase from 2015.

This fraud is particularly harmful to consumers. Javelin reported that account takeover costs consumers $263 on average, which is 5 times higher than other types of fraud.

Account takeover fraud is likely to continue its explosive growth, thanks largely to the corresponding explosion in data breaches. Consider some of the data breaches in recent years hitting some of the world’s largest organizations, including eBay, Target, and the Home Depot. The cost of these breaches can top $200 million.

Experts estimate that the average security breach costs an organization $200 per compromised cardholder. Since the average breach affects 28,000 card holders, the organization, on average, loses $5.6 million.

Criminals can easily access the data from these breaches via the dark web, and they are combining this raw information with increasingly sophisticated techniques to synthesize identities and evade detection.

Previously, fraudsters would quickly create and use a synthetic identity immediately. Now fraudsters take the time to build more credibility by obtaining a secured card or a mobile phone in the name of the new identity, and even applying for credit or creating social media accounts. Traditional rules-based systems are no match for this adaptive, evolving fraud.

Meanwhile, EMV standards have placed tougher security measures at points of sale. That means account takeover fraud is moving online. And with customers increasingly opening accounts across different channels, the digital ecosystem is seeing a proliferation of new vectors for fraud and risk.

The one thing that hasn’t changed: customer expectations. Customers expect to be onboarded quickly and easily, without having extensive checks and delays in opening an account. How can organizations today onboard more of the right customers and stop fraud, all while maintaining a positive customer experience?

In the face of all this fraud and change, organizations cannot afford to be reactive. What does it mean to be proactive? Machine learning combines high volumes of data with advanced analytics to connect the dots as a customer travels across different channels and payment types. These continuously updated hypergranular profiles mean that account takeover gets detected the moment it happens.

Tune in to the webinar, Combating Account Takeover  and ApplicationFraud Using Machine Learning Analytics, to learn how you can apply advanced machine learning and data science techniques to combat new account and account takeover fraud, all while ensuring a happy customer experience.