AI to Simplify Payments & Compliance: A Webinar Recap
Blog post from Basis Theory
Basis Theory’s Casey Clegg and DataBright’s Dwayne Gefferie discussed how AI is beginning to reshape payments through stronger security, compliance, fraud prevention, and transaction optimization, while noting that adoption remains gradual because effective models depend on large, responsibly shared datasets and raise concerns about misuse by fraudsters. Fraud detection is currently the most developed use case, with AI-powered risk scoring and chargeback-prevention tools analyzing hundreds of transaction variables to identify suspicious activity, reduce card-not-present fraud, and improve experiences for legitimate customers through technologies such as dynamic 3DS and CAPTCHA systems. AI is also increasingly important for anti-money-laundering efforts, where providers such as Feedzai, ComplyAdvantage, and Sentinels analyze behavioral patterns and money flows to flag potentially illicit activity amid a growing volume of digital transactions. Payment optimization remains less mature, largely focused on smart routing, payment flagging, and intelligent retries, although machine learning is expanding its capabilities. The discussion also highlighted that specialized micro-acquirers using AI may gain market share from established providers as technology, data collaboration, and regulatory demands continue to evolve.
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