Reference Architecture for Credit Loss Forecasting
Unify loan portfolios, economic scenarios and risk models on the Databricks Data Intelligence Platform to power scalable, transparent, auditable and cost-efficient CECL and stress testing.
What you’ll learn:
- An end-to-end lakehouse architecture for ingesting and unifying retail loans, commercial loans, general ledger (GL) and macroeconomic scenario data
- How Lakehouse Federation and Lakeflow Connect support secure, scalable and data integration across cloud and on-premises systems
- The use of lakehouse to standardize, reconcile and quality-check data for downstream model execution
- How to operationalize models built in Python, R or SAS using Mosaic AI and orchestrate workflows with Databricks Workflows
- A scalable compute layer using Databricks clusters to support large-scale CECL and stress testing
- A centralized data and model catalog, security model and controls with Unity Catalog to enforce data lineage, auditability and regulatory compliance
- How Lakehouse Apps enables secure collaboration, adjustments and forecast sign-off between credit risk and finance teams
Modernize Your Credit Loss Forecasting for CECL and Stress Test
- Portfolio data sources and ingestion
- Access and ingest retail loans, commercial loans and related exposure data
- Ingest GL data, including account counts and outstanding balances, for reconciliation and data integrity
- Use Lakeflow Connect for native, CDC-based ingestion from on-premises or cloud data systems, or leverage Lakehouse Federation for secure, scalable and duplication-free data access
- Macroeconomic scenario data
- Connect and source macroeconomic scenario data, such as Moody’s scenarios, via APIs
- Incorporate custom scenario expansion logic or ingest internal scenario datasets directly into the platform
- Data governance and management
- Utilize Unity Catalog to centralize metadata governance across portfolio data, scenario data, model outputs, overlays and disclosure reporting. Lineage tracking ensures data reliability and audit readiness.
- Enable multi-asset class integration, standardizing retail and commercial loan data with row-level access controls
- Perform data quality checks and GL reconciliation into curated Silver tables and sign off on data controls
- Leverage system tables and embedded audit trails for full auditability and compliance with regulatory standards
- Implement model execution
- Implement or import models developed in Python, R or SAS. Register models in MLflow.
- Define logic for variable derivation, model scoring and expected credit loss (ECL) calculations by scenario and time horizon
- CECL and stress test workflows
- Build workflows for scenario analysis, sensitivity analysis and attribution analysis
- Execute workflows at scale using Databricks Workflows, providing automation, monitoring and scheduling for complex model pipelines
- Business intelligence
- Use Databricks SQL to review and analyze portfolio data and scenario data
- Conduct loan-level credit loss analysis for each scenario and horizon
- Explore results interactively and validate assumptions with full transparency and traceability
- Credit risk and finance collaboration
- Enable real-time collaboration between credit risk and finance teams via Lakehouse Apps (web applications)
- Upload end user computing spreadsheets to support individual assessments
- Apply management overlays and sign-off controls, and integrate with downstream risk and finance systems for GL posting, disclosure reporting and more
Benefits
- Regulatory compliance and auditability
Ensure compliance with CECL, CCAR, IFRS 9 and other regulatory frameworks through automated data lineage, embedded controls and audit-ready workflows - Scalable performance for complex calculations
Run credit loss models and scenarios with ease using autoscaling Databricks clusters designed for compute-intensive financial workloads - Cost-efficient architecture
Leverage a consumption-based pricing model with no additional software licensing fees — resulting in lower TCO and flexible resource usage aligned to your demand - Secure, enterprise-ready platform
Built-in security, identity management and governance capabilities ensure sensitive risk data is protected and managed in accordance with enterprise and regulatory standards - Self-service with full customization
Enable internal teams to own and adapt their modeling environment through a self-service platform, while still supporting full customization, automation and integration with enterprise systems
