Predicting Banking Customer Needs with an Agile Approach to Analytics in the Cloud

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Moneta has repeatedly been recognized as the most innovative bank on the Czech market. This is due in large part to their strategy of completely shifting to the cloud and using data and advanced analytics to innovate the customer experience with use cases ranging from real-time recommendations to fraud detection.

In this talk, we’ll share how we migrated to the cloud to create an agile environment for analytics and AI. From rapid prototyping machine learning use cases to moving models into production, core to this approach was building a unified platform for data and analytics on Apache Spark, Databricks and AWS. Discussion topics include:

  • Moneta’s strategy and roadmap for moving to the cloud and creation of the data squad
  • Overview of use cases including ATM/branch location optimization using geo-data, digital channel attribution, identify fraud detection, etc.
  • Deep dive into the use of digital behavioural data (web, mobile app, internet banking) and offline transactions to understand and predict customer needs in near-real time using Spark MLLib
  • Approach to building the agile analytics platform and the specific challenges of using the cloud in a financial institution


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Jakub Masek
About Jakub Masek

MONETA Money Bank

About Milan Berka

DataSentics a.s.

Milan Berka is a ML architect at DataSentics a.s. After he finished his mathematics and stochastics college degree, he started pursuing a career of a data scientist. However, soon it became clear that without a proper data infrastructure and data engineering element, it is very difficult to make a lasting impact with any data science model - regardless of how great the model itself is. Therefore, almost four years ago, he jumped over to "more engineering side" and started building experience in cloud infrastructure, big data frameworks, DevOps practices and other engineering topics. Combining the machine learning and engineering knowledge, his primary focus now is designing and building solutions which ease or even enable the productionalization of machine learning models (MLOps).