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Demo: Lakehouse for Retail Banking: Credit Decisioning

Build your banking data platform and identify credit worthy customers

The Databricks Lakehouse 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 an end-to-end credit decisioning system for underbanked customers, delivering data and insights that would typically take months of effort on legacy platforms.

This demo covers the end to end lakehouse platform:

  • Ingest both internal and partner data, and then transform them using Delta Live Tables (DLT), a declarative ETL framework for building reliable, maintainable, and testable data processing pipelines.
  • Secure our ingested data to ensure governance and security on top of PII data
  • Build a Machine Learning model with Databricks AutoML to identify credit worthy customers
  • Leverage Databricks DBSQL and the warehouse endpoints to build dashboard to analyze the ingested data and explain the machine learning model outputs
  • Orchestrate all these steps with Databricks Workflow

Installing the demos

%pip install dbdemos
import dbdemos
dbdemos.install('lakehouse-fsi-credit')


Databricks demos components

Delta Live Table
Data Science
Unity Catalog
BI/DW/DBSQL

Assets available in the Databricks demo


Delta Live Table pipelines:

dbdemos databricks delta live table DLT demo example lakehouse-fsi-credit

DBSQL Dashboards:

CreditDecisioning
dbdemos databricks DatabricksSQL dashboard demo example CreditDecisioning