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Lower False Positives in Financial Crimes using LLM/ML and Spatial Analytics

Lower False Positives in Financial Crimes using LLM/ML and Spatial Analytics

Increase accuracy and lower false positives in Financial Crimes. Register to learn how AI & automation can sharpen your investigations.

When User Testing Isn't Possible

There’s no way around it: user testing is vitally important in software development. So what options do you have when, for whatever reason, you can’t speak with end users to validate what you’re building? Join us as we discuss these challenges and share some helpful methods we’ve employed when navigating these constraints.

ZooKeeper Usage 2: Observable<ZooKeeper>

In the last post, we used ZooKeeper as a service registry. When services started, they registered with ZooKeeper at a pre-agreed place. (/services/{dataset-name}). Clients could list the data servers available and decide which ones to connect to, or request that new ones could be launched. Thanks to ephemeral nodes, servers can crash and their registry entries are automatically deleted. Today we’ll talk about three use cases for watching changes in ZooKeeper.

Fraud: Detection & Avoidance in Banking

Transaction investigation and treasure mapping with KPIs using graph technology, high performance search, and high performance analytics.