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

Predicting Banking Customer Needs with an Agile Approach to Analytics in 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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