Success story / Computing anti-fraud predictors

High-speed platform for fraud detection in Česká spořitelna (Erste Group)

Fully automated processing

1.5 billion transactions daily

Scalable and customizable

Project Brief

Implementing a high-speed big data platform for computing anti-fraud predictors.

The whole platform is integrated with all required systems. It is the unique solution, the first Apache Spark implementation for production data computation in our Data Lake distributed environment.
Martin Gerneš
Tribe Lead responsible for data at Erste Group (Česká spořitelna)

Challenge

Like banks, Česká spořitelna, the Czech arm of the Erste Group, needs to monitor transactions to confirm whether they appear normal or suspicious. This process uses statistical data – predictors – to automatically process transactional data and flag up suspicious transactions.

The bank’s solution, based on a traditional relational database, was not fit for purpose. They could not process the transaction history within the limited computational window each night.

Quick fraud detection is essential for minimising losses, so the team at Česká spořitelna were keen to implement a solution. They were aware that Apache Spark implementation on the Hadoop cluster was one potential approach, but they did not have the in-house expertise to execute this solution.

Business needs

The solution needed to meet the following specifications:

Have the ability to perform high-speed computations of predictors within a limited time window
Allow in-house departments to design and adjust the computed predictors
Easy integration with the surrounding banking systems
Scalability for future extensions and customization

Fraud detection solution

Profinit built a custom-made big data computational platform, based on the Hadoop, Apache Spark and Python technological stack. We worked with the bank’s in-house data lake environment department to design proper data architecture, which included the creation of a new dedicated data mart. This custom-built solution is the first of its kind within the client’s infrastructure. It’s scalable and integrates with all required systems.

Incorporating big data technologies

This unique architecture was built to perfectly match the requirements of the in-house analytics department. The core of the application is based on big data technologies, but all computations are defined using SparkSQL. This allows the in-house credit risk, fraud detection and business intelligence departments to fully understand the computational processes, and it allows them to design, implement and adjust any new predictors.

Tech stack

Hadoop
Apache Spark
Python
SparkSQL

Project Summary

We implemented a high-speed big data platform for computing anti-fraud predictors, and achieved these results for the bank:

Česká spořitelna has a new high-speed solution which fulfills the requirements of the limited-time window
The solution is scalable – it can process larger data volumes and conduct faster computations in the future
The bank’s in-house departments can customize the computations
The big data platform integrates fully with all required banking systems

Would your bank benefit from accessing this cutting-edge technology?

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