--- title: Sardine AI Lab | Pesquisa aplicada sobre crimes financeiros source_page: https://www.sardine.ai/labs canonical: https://www.sardine.ai/labs format: text/markdown description: O Sardine AI Lab é um grupo de investigação aplicada que estuda se os modelos conseguem aprender a estrutura do comportamento financeiro suficientemente bem para detetar ataques que nunca foram ensinados a identificar. --- **Quick links:** [Human page](https://www.sardine.ai/labs) · [Home](https://www.sardine.ai) · [Customers](https://www.sardine.ai/customers) · [Blog](https://www.sardine.ai/blog) · [Demo](https://www.sardine.ai/demo) --- # Sardine AI Lab | Pesquisa aplicada sobre crimes financeiros O Sardine AI Lab é um grupo de investigação aplicada que estuda se os modelos conseguem aprender a estrutura do comportamento financeiro suficientemente bem para detetar ataques que nunca foram ensinados a identificar. ## Build the models financial crime We study how AI can learn the language of financial behavior, anticipate new attacks, and help risk teams make better decisions without losing control. ## Understanding the language of financial crime Sardine AI Lab is an applied research group focused on a hard question: can models learn the structure of financial behavior well enough to catch attacks they were never explicitly taught to find? We combine sequential transaction data, device intelligence, behavioral biometrics, and identity signals across Sardine’s global network. The goal is simple: turn research advances into measurable protection against financial crime. ## Built on the fastest-growing dataset in financial crime Our research starts with the breadth of Sardine’s network and goes deep into the sequence of each customer’s financial behavior. - 7.8B: Devices profiled - 441M: Consumer identities - 3.1M: Businesses screened - 5.46B: Transactions analyzed - $1.6T: Transactions protected ## Four questions shaping our work Financial crime is sequential, adaptive, and connected. Our research agenda is built around those realities. ## A stronger signal from the history already there Our first payments foundation model learned from unlabeled transaction sequences, then supplied new features to the issuing fraud model already in production. - 24–35%: more fraud caught at the same share of transactions flagged for action - 68%: improvement in overall ranking quality, measured by AUC-PR - 29%: improvement for a client population held out of pretraining ## Bring us the hard problems We want to widen who gets to work on financial crime research and make meaningful progress easier to measure. ## Ideas, methods, and evidence Read the work behind the numbers and follow the questions we are investigating next. ## The next breakthrough in fighting financial crime may start outside our walls