Page 31 - gs260802
P. 31

Insights and Expertise



        tively, AI tools need to have been fed vast repositories of   or removed from processing. The answer can't depend
        historical merchant data and then trained by expert ana-  on a score alone. AI-driven merchant risk programs can
        lysts.                                                  provide an extensive audit trail that shows how decisions
                                                                were reached.
        Common problems with general-purpose AI include cold
        starts, where insufficient training makes AI models un-  The record makes clear which evidence shaped the out-
        aware of risks outside the mainstream. Another pitfall is   come and where human judgment entered the process.
        gray areas, where agents can't distinguish products and   Underwriting will remain an essential component of mer-
        services across jurisdictions. Through adversarial testing,   chant risk management, but approval is no longer the
        bad actors find the thresholds of AI moderation logic, then   endpoint. Payments companies need decisioning models
        find "bypasses" (for example, leetspeak, which substitutes   that continue to monitor for meaningful changes after on-
        numbers or symbols for letters) to exploit.             boarding and provide risk professionals with a defensible
                                                                basis for action.
        Without  sufficient context,  agents can  spawn  false posi-
        tives and negatives; without controls, they operate with-  Agentic AI can strengthen merchant risk decisioning
        out guardrails                                          when it's built on merchant-specific intelligence and gov-
                                                                erned by clear boundaries. Used that way, it gives risk pro-
        AI-supported risk systems need intelligence that reflects   fessionals a better way to support growth while keeping
        how merchant risk actually appears in the payments sys-  hidden exposure from accumulating in the portfolio.
        tem. This intelligence, known as context in AI systems,   Dan Frechtling is a veteran risk leader serving as senior vice president
        comes from regulations, card brand rules, enforcement
        patterns, knowledge of bad-actor networks, and human-   of Product and Strategy at LegitScript,  https://legitscript.com, where
        in-the-loop feedback from experienced analysts..        he  drives  product  innovation,  grows  the  company's  expertise  in  mer-
                                                                chant intelligence, and strengthens partnerships with leading platforms,
        Faster decisions still need a record                    marketplaces, and payment companies. He brings over a decade of
                                                                experience in mitigating merchant, seller, and advertising risk, having
        Speed  only  helps  when  a  payment  company  can  stand   held leadership roles such as president of G2 Risk Solutions and CEO of
        behind its decisions. A bank, card network or regulator   Boltive. Contact him via LinkedIn at linkedin.com/in/frechtling.
        may later ask why a merchant was approved, restricted
   26   27   28   29   30   31   32   33   34   35   36