Databricks Intelligence Platform for Retail Banking: Fraud Detection

The Databricks Intelligence Platform is an open architecture that combines the best elements of data lakes and data warehouses. In this demo, we’ll show you how to build a real-time fraud detection system for banking transactions, delivering data and insights that would typically take months of effort on legacy platforms.

This demo covers the end-to-end lakehouse platform:

• Ingest data from external systems (EPR/Salesforce) and then transform it using Delta Live Tables (DLT), a declarative ETL framework for building reliable, maintainable and testable data processing pipelines
• Secure your ingested data to ensure governance and security on top of PII data
• Leverage Databricks SQL and the warehouse endpoints to build dashboards to analyze the ingested data and understand the existing fraud
• Build a machine learning model with Databricks AutoML to flag transactions at risk
• Leverage Databricks Model Serving to deploy a REST API serving real-time inferences in milliseconds with model A/B testing
• Orchestrate all these steps with Databricks Workflows

 

To install the demo, get a free Databricks workspace and execute the following two commands in a Python notebook

Dbdemos is a Python library that installs complete Databricks demos in your workspaces. Dbemos will load and start notebooks, Delta Live Tables pipelines, clusters, Databricks SQL dashboards, and warehouse models … See how to use dbdemos

 

Dbdemos is distributed as a GitHub project.

For more details, please view the GitHub README.md file and follow the documentation.
Dbdemos is provided as is. See the License and Notice for more information.
Databricks does not offer official support for dbdemos and the associated assets.
For any issue, please open a ticket and the demo team will have a look on a best-effort basis.