Real-Time Fraud Detection at Play
Play needed to replace its batch fraud detection with a real-time system. The new system had to handle high-volume telecom data and let fraud experts build detection algorithms in a visual tool.
The challenge
Play needed a fraud detection system that would allow:
- Rapid development and customization to combat evolving fraud
- Real-time detection (replacing their existing batch system)
- Handling of high-volume telecom data streams using tools like Flink
- Non-technical fraud experts to develop detection algorithms through visual tools, without needing to understand streaming or complex programming
The key requirements were quick adaptability, real-time processing, scalability for telecom data, and usability for domain experts.
Solution highlights
- Eliminated the delay in fraud detection from batch processing
- Addressed complex SIM card fraud schemes that were hard to detect
- Reduced financial losses from fraudulent activities
- Solved the challenge of managing complex fraud detection rules
Nussknacker Fraud Detection Features
Tool for non-technical users
Nussknacker simplifies complex technical operations through its visual interface. Users can build decision algorithms using intuitive graphs instead of code, while core business rules are managed through easily modifiable decision tables. This enables fraud teams to focus on detection strategies rather than technical implementation, with built-in support for Flink processing, time windows, data enrichment, and API integration.
Teams can focus on fraud detection, rather than intricacies of Flink development
Play's fraud team can now rapidly implement detection scenarios without technical barriers, allowing them to prioritize fraud intelligence and strategy over implementation details.
Sensitivity tuning and obfuscation
Nussknacker's expression language enables complex detection thresholds and obfuscated rules, making it difficult for fraudsters to reverse-engineer while allowing flexible sensitivity settings.
Ease of experimentation
Nussknacker enables rapid experimentation with detection algorithms. Teams can create and deploy variations within minutes, test on data subsets, and implement changes without IT support, allowing for immediate feedback and iteration.
Fitted to challenges
Nussknacker processes streaming CDR data from Kafka with high throughput and low latency, while enabling easy data enrichment from external sources. Built on Flink, it supports time-window analysis for detecting multiple fraud types including:
- SPAM
- SIM cloning
- SIM boxing
- IRSF fraud
- Wangiri
- High usage and Bill Shock
- Bursts of untypical usage
Fraud Detection Resources
How to simplify the integration of ML models into business applications, automate many of the technical complexities, and support advanced techniques like A/B testing and ensemble models. A fraud detection example.
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Are You Faster Than the Fraudsters?
Detect and block fraudulent activity in real time with Nussknacker's low-code platform. Automate decisions, reduce false positives, and act on anomalies instantly. Contact us to start building your defense today.