Challenges of manual fraud triage at PicPay
PicPay aims to make <a href="https://bitcomme.com/financial-services-support-hub-celebrates-grand-opening/” title=”Financial Services Support Hub celebrates grand opening”>financial services in Brazil simpler and more accessible. As PicPay expanded its use of AI, the fraud prevention team saw an opportunity to improve its evaluation of anomalous transactions.
Before adopting Genie Agents, PicPay’s fraud detection relied on statistical anomaly triggers. These triggers compared past transaction behavior to a predicted baseline. When the volume or value for a given time window exceeded expectations, the system generated an alert. But because the triggers evaluated statistical deviation rather than broader behavioral context, they produced a high number of false positives. Each flagged case required a human analyst to open a dashboard and manually decide whether to intervene.
“Manual triage is inherently sequential, one case at a time, no matter how experienced the analyst is,” said Flávio Arena, Tech Manager at PicPay.
This created three compounding limitations: slow response times; variability in the analysis process due to differing case contexts and available signals; and a scaling challenge as monitoring expanded. Expanding monitoring to new products would have required scaling the team proportionally, pulling analysts away from higher-value investigative work. As monitoring expanded, the volume of anomalies became unsustainable, and PicPay’s fraud team started looking for an autonomous alternative.
PicPay initially prototyped an autonomous agent internally. As the team refined the approach for production deployment, it adopted Genie’s Agent mode and migrated its rule and validation framework to Genie Agents.
