The current industry stats trends on fraud and risk are alarming. Max Spivakovsky, Senior Director of Global Payments Risk Management with Galileo, addressed the issues during a recent webinar on risk mitigation.
“Card fraud will result in a staggering $400 billion in losses globally in the next decade, according to a report from Nilson,” said Spivakovsky.¹ “The U.S. accounted for 36% of all global card fraud losses in the last year. By the end of this decade, the expected U.S. losses will total over $17 billion.”²
He went on to quote more stark figures from a LexisNexis study. “Identity fraud rates are up over 45% since the beginning of the pandemic. $1.00 of fraud represents $3.60 in lost revenue.”³
While these numbers are concerning, there are solutions. “This is a major challenge that needs to be addressed, as there has been underinvestment in fraud mitigation,” he said. “Here is where Galileo’s Dynamic Fraud Engine can help with our holistic approach.”
The Dynamic Fraud Engine (DFE) offers an integrated risk strategy solution: Focusing on reducing risks for operational and transactional fraud, as well as providing customer and dispute service tools. The goal is to reduce clients’ fraud mitigation while improving ROI.
“Our DFE looks at a client’s customized end to end experience in managing fraud losses with custom rules and models. We use key trends from the industry as well as consortium data, as we have data on over 100 million spend patterns,” explained Spivakovsky. “We are also very invested in new product development to tailor our solutions to our clients.”
When clients implement the DFE, they can expect fraud insights, intelligence, and report dashboards. “Clients receive risk data for better decisioning. They will have updates on fraud rules and strategies, so that they can maximize good transactions and minimize fraud,” he said.
The DFE can reduce fraud transactions significantly. How? Spivakovsky explained, “The DFE provides new, real time risk analytics and strategy assessment capabilities. Rules, merchants, location activities, and so on that comes in through the network. We use additional models to perform assessment and monitoring for our clients, which have been pre-tested.”
How can client fraud teams best work with the DFE? “We can implement either an outsourced approach or a hybrid approach with a client’s in-house team, depending on a client’s needs,” he said.
How long does it take for rules and machine learning algorithms to learn a client’s customer portfolio, spending and buying habits? Right away, according to Spivakovsky. “Machine learning starts immediately with one transaction of any kind of client activity. We are also well equipped with machine learning models.”
Quoting statistics of 15% growth of fraud during the pandemic, he broke down the driving factors. “There’s the cost of time when battling fraud. The cost of resources. The cost of recovery.”
“Think about mitigating fraud with a proactive maintenance approach, the right layers, the right mitigation controls, and machine learning,” Spivakovsky said.
“It’s like maintaining your car with $50 oil changes instead of spending $5,500 to replace your engine later.”
¹ Nilson Report, December 2021, Issue 1209, Page 6 https://nilsonreport.com/upload/content_promo/NilsonReport_Issue1209.pdf
² Nilson Report, December 2021, Issue 1209, Page 5 https://nilsonreport.com/upload/content_promo/NilsonReport_Issue1209.pdf
³ LexisNexis Risk Solutions 2021 True Cost of Fraud Study, Pages 6, 14 https://risk.lexisnexis.com/-/media/files/financial%20services/research/lnrs_true-cost-of-fraud-financial-services-and-lending-2021_research.pdf
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