Expanded Payment Method Insights, Updated Machine Learning Models, and UX Improvements Provide Fintechs with Comprehensive Control in the Fight Against Fraud
Sift, the leader in Digital Trust & Safety, announced new platform capabilities that simplify digital risk assessment across multiple payment methods, while empowering fraud teams with more control in payment fraud decisioning. The announcement was made at Money20/20 USA in Las Vegas, where Sift is a 4 star sponsor exhibiting at booth #4208.
According to EY, alternative payment method volume reached $19 trillion globally in 2022, while businesses continue to grapple with the rapid adoption of Buy Now, Pay Later (BNPL) and a wider range of Payment Service Providers (PSPs), digital wallets, and ACH systems—all of which deliver greater value and flexibility to users. Yet these new methods present challenges: as businesses provide more payment options and modernize the customer experience, they expose themselves to new types of risk. To address these challenges, Sift is introducing a range of features specific to the payments ecosystem: expanded payment coverage, weighted cohort ML modeling, and a customizable UX.
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Expanded Payment Coverage
Sift has expanded its payment coverage by introducing non-traditional payment automation, investigation, and ML model training.
For example, if a fintech company wants to expand the ability to facilitate a broader range of payment methods and understand how end-users make payments, its fraud team can now leverage insights across a variety of payment methods, including ACH, wire, SEPA, prepaid cards, and crypto-specific transactions (such as digital wallets)—all directly within the Sift Console.
This data can be used to inform decisioning by incorporating criteria from these extended payment options, and by performing risk assessments on all payment channels. Furthermore, each Sift customer’s custom machine learning model can now also incorporate insights from these extended payment options.
Multi-tiered Cohort ML Modeling
Sift has enhanced its machine learning capabilities to adapt to evolving payment trends by introducing an ensemble of multi-tiered machine learning models, which encompass similar customer cohort models alongside both global and custom models.
This approach enables fintechs to benefit directly from Sift’s global network while also focusing specifically on analyzing fraud patterns that are unique to the fintech industry and emerging payment trends.
Configurable User Experience
Sift customers can now easily tailor the payment fraud prevention experience for traditional and emerging payment signals, with the ability to drag and drop risk data into a personalized format within the Sift Console for accelerated case resolutions.
“The payments ecosystem has undergone a transformation in recent years,” said Sift CTO Neeraj Gupta. “By providing fintech companies with deep insight on emerging payment methods and their different risk profiles, they can better serve their own customers while further reducing fraud and maximizing revenue.”
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