This top fintech company got an intelligent customer clustering process and behavioural analysis.
This company had the challenge of ingesting real-time data from components such as payments, transactional layer, behavioural data while maintaining efficiency for the online banking platform.
They wanted a solution to maintain high data quality for analytics, the speed requirements for data science and do it all at a low cost.
What they needed was a high-volume data storage that will allow them to run many scheduled batch jobs to constantly update the different machine learning models, while serving the objectives of multiple teams. After a detailed research of the marketplace, they reached out to the Voyance team for their needs.
Voyance Pipelines helped them reduce the time required to deploy end-to-end workflows to drive analytical insights.
Less time was spent on setting up and managing a data infrastructure. This helped them to focus on addressing customer and market demand by migrating new use cases seamlessly into production.
Using Voyance ML, they reduced errors and the amount of time spent on processes and created more value for customers, thus building a foundation for future growth
This open banking platform was able to decrease the processing time, increase compliance and increase conversion. Specifically, they got:
A straightforward implementation of new data pipelines including existing data sources and real-time data.
Ability to ingest raw data from structured and unstructured sources and query alongside other data sources for deeper exploration.
Reduction in manual work required for interactive experiments and batch jobs.
Easy exploration of big data and machine learning Initiatives with a purpose-built foundation.
Simplified ETL and feature generation tasks with Voyance Workflow engine.
Leveraged Voyance ML and data pipeline management to build advanced analytics for greater insights.
Built end-to-end workflows for data access, optimized resource management, and ETL.