PREVENTING FRAUD WITHIN MILLISECONDS
Like all banks, one of Belgium’s biggest banks is constantly faced with new fraud threats whereby criminals are trying to steal from their unsuspecting clients. Where traditional payments typically require one business day to complete, Europe wants to make instant payments whereby transactions happen within 10 seconds common practice.
Behind the scenes
We created a high-level domain-specific language (DSL) workbench using Apache Flink.
This enables the bank’s low-code data scientists to easily interact with a complex streaming data processing pipeline for online payment fraud detection.
The app comes with a local continuous integration / continuous deployment (CI/CD) environment to develop, test, and analyze data in a scalable technology.
- Apache Flink
- Continuous integration & deployment (CI/CD)
- Domain-Specific Languages
- Real-time analysis
- Continuous Integration
- Continuous Deployment
“In this project, the challenge was to allow a low-code data scientist to work on a high-performance, complex streaming data processing pipeline.
To achieve this, we abstracted the complicated plumbing (connection and joining of data streams) out of the business logic, and then we geeked out by creating a Domain Specific Language (DSL) in Scala that the data scientist could use to succinctly specify the business logic.”
”The fun bit for me was the whole domain specific language specification and implementation: very geeky, technical yet creative work.Dominique ChanetLead Architect
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