Accelerator 01 — Banking

The Data Foundation
Every Bank Needs.
Pre-Built.

A production-tested dimensional data model covering retail, corporate, and treasury banking — with a dedicated regulatory layer for BCBS 239, IFRS 9, and COREP. Reduce design time by up to 60% and start with a model that has already been proven in production.

Retail BankingCorporate BankingTreasuryBCBS 239IFRS 9COREP / FINREPPillar 3AMLCustomer 360
Delivery Package Includes
Logical & physical model documentation
DDL scripts for target platform
Source-to-target mapping templates
Data quality rule library (200+ rules)
Business glossary for all CDEs
Regulatory layer configuration guide
300+
Pre-built entities
200+
DQ rules included
60%
Design time saved
3
Platform variants
Business Value

Why This Accelerator
Moves the Needle

Most banking data warehouse projects spend months in the design phase — mapping source systems, resolving definitional conflicts between business units, and debating entity relationships that have standard answers. Our Banking Data Warehouse Model eliminates this phase entirely.

The model carries 10+ years of banking data engineering decisions — proven entity relationships, regulatory data structures, and data quality rules that have been tested against real banking data in production environments across multiple geographies.

Whether you are building a new DWH from scratch, migrating a legacy on-premise warehouse to the cloud, or adding a regulatory reporting layer to an existing platform, this accelerator compresses the most expensive and risky phases of the project.

50–60% Reduction in Design Phase
Pre-built entity relationships and regulatory structures replace weeks of blank-page modelling. Your team starts adapting, not designing from scratch.
Regulatory Layer Ready on Day One
BCBS 239 risk data aggregation, IFRS 9 ECL input structures, and COREP / FINREP reporting entities are pre-built and documented — not retrofitted after the fact.
Lower Implementation Risk
Every entity relationship and data quality rule in the model has been tested in production. You inherit proven patterns — not experimental design decisions.
Platform Agnostic
Available in variants for Azure Synapse, Databricks Lakehouse, and Snowflake — with DDL scripts and transformation templates for each target platform.
Model Scope

What the Model Covers.

The Banking DWH Model spans retail, corporate, and treasury banking — with cross-cutting customer, regulatory, and channel data layers.

Customer & Party
Customer 360 golden record
Party & relationship hierarchy
KYC & identity attributes
Channel & interaction history
Household linking
Products & Accounts
Current & savings accounts
Term deposits & CDs
Retail loans & mortgages
Credit cards & overdrafts
Corporate facilities
Transactions & Events
Transaction history
Payment & transfer events
Fee & interest events
Trade & settlement
Collateral & security
Regulatory Layer
BCBS 239 risk data aggregation
IFRS 9 staging & ECL inputs
COREP / FINREP report structures
Pillar 3 disclosure data
AML transaction monitoring
Analytics & BI Layer
Customer profitability
Product performance
Channel analytics
Churn & CLV features
Campaign performance
ML Feature Layer
Credit scoring features
Fraud detection signals
Churn propensity inputs
Next best offer features
Lifetime value inputs
Implementation

From Accelerator
to Production.

The accelerator is the starting point, not a finished product. CALIGO adapts it to your source systems, technology stack, and regulatory requirements — dramatically compressing the design and build phases.

01
Source System Assessment
We map your source systems — core banking, CRM, payments, treasury — against the model entities. Gaps are identified and adaptation scope is defined in week one.
02
Model Adaptation
The model is adapted to your naming conventions, additional entities, and target platform. Core regulatory structures are preserved; edges are shaped to your context.
03
Pipeline & Transformation Build
CALIGO engineers build ingestion pipelines and dbt transformations using the adapted model as the target. Pre-built patterns accelerate development at every stage.
04
DQ & Reconciliation Testing
Data quality rules from the model library are applied and validated. Full reconciliation against source system totals before go-live.
05
Documentation & Handover
Complete data dictionary, lineage documentation, and operating procedures. Your team takes ownership of a fully documented platform.
Typical Impact vs. Greenfield
Design phase vs. greenfield
50–60% faster
DWH entities pre-designed
300+
Data quality rules included
200+
Regulatory frameworks covered
6
Typical project time saved
6–10weeks
See the Banking Model in Your Environment.
We run a structured source system assessment to show exactly how the Banking DWH Model maps to your data — and what adaptation is needed. Typically completed in one week.