Oscilar is developing an AI-driven platform to help financial institutions detect and prevent online transaction fraud. The company emphasizes machine-learning models that integrate aggregated anonymous risk signals from across a customer network and are continuously trained via an automated feedback loop. Oscilar says its models require less first- and third-party fraud data from customers to train and that customers retain visibility into model inputs and parameters. The product includes a no-code toolkit for risk operations teams to create, test, monitor, and deploy models without engineering intervention, with humans in the loop during onboarding to check performance and bias. Oscilar emerged from roughly two years of stealth, has publicly launched, and claims to be working with "dozens" of fintech customers. The team includes dozens of engineers hired from Facebook, Google, Uber and Confluent. Financially, the company is entirely self-funded with $20 million contributed by its co-founders.

Total raised

$20M

Funding rounds

1

Latest round

Equity

Latest activity

Mar 2023