Machine Learning credit card dispute
Non-trivial filtering, aggregation, and decision logic applied to the models’ results
Case studies
Nussknacker can be used where business teams want to define and run real-time decision algorithms, like in marketing, monitoring, Internet of Things, next best action, fraud detection, and more
Data streams and fraud detection
Analysts should be able to configure the system themselves, deploy new rules and monitor their performance. Professional work also requires testing the set rules beforehand since blocking regular clients should be avoided at all costs
Real time marketing for a Telecom Service Provider
The Client needed both very high throughput and the ability to analyse events in time windows. After the competitive evaluation, the real-time marketing decisioning algorithms were implemented with Nussknacker.
Data-driven marketing
Keeping up with the new conditions requires changes not only in terms of the approach to campaign creation, but also to the implementation process
Fraud Detection Solution for a Telecoms Provider
In Nussknacker it is easy to set up obfuscated detection thresholds to prevent easy reverse engineering by fraudsters. Nussknacker uses the same expression language to formulate conditions for sensitivity thresholds, ranging from very simple to very complex.
Real-time demo
Here are working demos with real-time data processing. For more details check the blog posts