Data engineering Data science Use case
the banking sector challenges

Digazu’s platforms answer the banking sector challenges

International bank customers expect their bank to offer online immediate payment services. Banks deal with more than 75 million messages per day to answer their customers needs. Those messages must be managed in real time (including failure detection) to make the “real-time transfer of funds” and 24×7 availability possible. The banking sector needs to step up its data management. It needs to manage high volumes of real-time data to anticipate, detect and process problems such as fraud and anomalies, failure detections, etc. The banks need the right infrastructures to reach high performance, high availability and low latency. 

Digazu’s platforms (end-to-end data engineering & end-to-end  data science platforms) are perfect to answer their challenges: 

    1. all the data sources are registered and collected once: when data scientists come up with a new model, IT people do not have to collect the data sources again. It is done once and for all.
    2. all data science models and banking applications for analytics that need real-time data are fed by streams. It is made possible through the data consumption registration interface.
    3. Digazu’s platforms enable dealing with very large volumes of data without overloading operational systems or the data lake. The platforms have an orchestrator that takes every request in charge, playing the role of a broker between data sources and targeted destinations, or between the data lake and targeted destinations. This feature enables higher availability, low latency and higher-performance.

These were the three key assets where Digazu’s platforms help the banking sector dealing with real-time banking transactions and streamlining data between data producers and data consumers. The platforms suit high-performance requirements of the banking sector thanks to its unique orchestrator that makes data available for data scientists, models and applications, in batch and real-time, to anticipate, detect and process problems such as fraud, anomalies and failure detections.