Overview
Effective risk management requires real-time visibility into credit, market, operational, and liquidity risks. Legacy systems often fail to provide the speed and granularity needed for modern regulatory requirements and fast-moving markets.
Root Cause Analysis
Why Risk Systems Underperform
- Batch Processing Delays: Risk calculations running overnight miss intraday market shifts.
- Fragmented Risk Views: Credit risk, market risk, and operational risk managed in separate systems with no unified dashboard.
- Regulatory Complexity: Basel III, Dodd-Frank, and CECL require granular data lineage that legacy systems can't provide.
- Model Governance Gaps: Models are deployed without proper versioning, validation, or monitoring.
Use Cases
1. Real-Time Credit Risk Scoring
Challenge: A commercial bank's credit scoring model ran in batch, missing real-time economic signals. Solution: Deployed a streaming analytics platform on Apache Kafka and Databricks with real-time feature engineering from market data, transaction patterns, and macro indicators. Result: Credit decisions now incorporate data that is less than 15 minutes old, reducing default rates by 18%.
2. Integrated Risk Dashboard
Challenge: A wealth management firm had separate tools for market risk (VaR), credit risk, and compliance. Solution: Built a unified risk data platform on Snowflake with Power BI dashboards providing a single pane of glass across all risk categories. Result: Risk committee meetings reduced from 4 hours to 1 hour with better decision quality.
3. CECL Compliance Automation
Challenge: A mid-size bank needed to implement Current Expected Credit Losses (CECL) modeling with full data lineage. Solution: Implemented a dbt-powered data pipeline with Great Expectations for data quality, feeding ML models on Azure ML for lifetime loss estimation. Result: Full CECL compliance achieved 6 months ahead of deadline with auditable data lineage.
Cloud Technologies We Use
- Apache Kafka — Real-time streaming for market and transaction data
- Databricks — ML-powered risk models and feature engineering
- Snowflake — Centralized risk data warehouse
- Azure ML — Model training, deployment, and monitoring
- Great Expectations — Data quality validation for regulatory compliance
- Monte Carlo — Data observability and lineage tracking