Anti-fraud startup Sardine makes a splash with Series B close

Anti-fraud startup Sardine makes a splash with Series B close

Sardine, a provider of fraud, compliance and instant settlement solutions, has closed its Series B round on $51.5m in its Series B.

Finnovation Online Panel on ACH Fraud

Hear from 4 CEOs about ACH fraud, how it has changed, and how to stop it. This video is part of the Finnovation Online Series by Sila.

Fintech Fraud Prevention: Tips & Best Practices from Leading Experts & Operators

Laura Spiekerman, Co-Founder & Chief Revenue Officer, Alloy
Eli Polanco, Founder & CEO, Nivelo
Ohad Samet, Co-Founder & CEO, True Accord and Author of “Introduction to Online Payments Risk Management”
Shamir Karkal, Co-Founder & CEO, Sila

Learn the tools, patterns, techniques, and technologies employed by fintech by fintech experts to detect and reduce fraud.


Fintech Fraud Prevention 101: What are the most common fraud tactics and what best practices should companies have in place in their organization?

Anti-Fraud Metrics: What are the KPIs and benchmarks for implementing a robust anti-fraud program for a Fintech Startup Vs. Fintech at Scale.

Humans Vs. Bots: What roles are required to support a technology-led anti-fraud program.

User Experience: How do you tune User Experience with Anti-Fraud requirements to maximize registration and usability.

SEON Fraud Prevention Explained – What Is SEON, and How Does It Work?

SEON offers fraud prevention based on three key principles: better data, real-time monitoring, and Machine Learning intelligence. See how it can help your business today.

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Wanna see how SEON can help your business reduce fraud?

Try it for FREE here:

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Fraud Prevention | AI in Finance

Can AI be used for fraud prevention? Yes! In this video, we’ll go over the history of fraud prevention techniques, then talk about some recent AI startups that are helping business reduce credit card fraud. We’ll break down what the different AI models that help with fraud prevention look like (decision trees, logistic regression, neural networks) and finally, we’ll try it out on a transaction dataset.

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