Financial Risk Analytics

Part of the worlds-biggest-software-project initiative.

An AI-native, cloud-native open-source platform for portfolio risk measurement, stress testing, and regulatory capital reporting.

Financial Risk Analytics is a candidate project to build an accessible alternative to enterprise risk platforms. It targets banks, asset managers, insurance companies, and trading firms that need rigorous Value at Risk computation, Basel III/IV stress testing, counterparty credit risk monitoring, and regulatory capital calculation but cannot justify the cost or implementation complexity of incumbent enterprise systems.

Why Financial Risk Analytics?

Mid-tier institutions still rely on fragmented spreadsheet workflows or on-premises systems that cannot scale to intraday reporting cadences required by modern regulation.
Enterprise platforms such as BlackRock Aladdin, MSCI RiskManager, Axioma, and Moody's RiskAuthority are prohibitively expensive for regional banks, hedge funds under USD 1B AUM, and insurance companies.
Proprietary factor models in incumbent platforms are black boxes; institutions struggle to explain risk numbers to boards, regulators, and auditors.
Linking trading book (FRTB), banking book (IRRBB, Basel IV), and accounting (IFRS 9) within a single coherent data model is poorly served across the market.
Legacy platforms use proprietary data models with limited open integration to modern data platforms (Snowflake, Databricks, dbt).

Key Features

Portfolio Risk Measurement

VaR computation via Historical Simulation and parametric methods across equities, fixed income, and vanilla derivatives
Monte Carlo full-revaluation VaR for non-linear instruments (options, structured products)
Expected Shortfall and liquidity-adjusted VaR
Risk factor decomposition: attribution of VaR to position, sector, country, and key risk factors

Stress Testing & Scenario Analysis

Pre-built historical scenarios including 2008 GFC, COVID-19 2020, and the 2022 rate shock
User-defined factor shocks and correlated stress tests
AI-assisted scenario generation from natural-language macroeconomic descriptions
Walk-through historical period replay for path-dependent portfolio behaviour

Counterparty & XVA Analytics

Counterparty credit risk exposure simulation, netting, and collateral modelling
CVA and FVA calculation for OTC derivative portfolios
KVA and MVA computation as part of the full XVA suite (backlog)

Regulatory Capital & Reporting

Basel III RWA calculation under the Standardised Approach
IFRS 9 expected credit loss (ECL) estimation
FRTB Standardised Approach sensitivity-based method (SBM) calculations
Role-based dashboards for risk managers, portfolio managers, and compliance officers

Developer Integration

REST API and Python SDK for programmatic access
Pre-built connectors for LSEG Workspace and Bloomberg B-PIPE market data
Configurable yield curve and volatility surface ingestion
Integration with Snowflake and Databricks for analytical pipelines

AI-Native Advantage

The platform applies AI to areas where incumbent systems leave gaps. LLMs translate natural-language macroeconomic narratives into quantitative risk-factor shocks, removing the need for quant intermediaries when constructing scenarios. ML models detect anomalies in daily risk metrics before limit breaches, and unsupervised learning identifies latent credit concentration risks across complex counterparty networks. Automated natural-language risk commentary turns quantitative outputs into board-ready summaries.

Tech Stack & Deployment

The platform is designed cloud-native with a permissive open-source foundation. Pricing and risk libraries are layered on QuantLib (Modified BSD), OpenGamma Strata (Apache 2.0), and the Open Source Risk Engine. Distributed Monte Carlo simulation runs on Apache Spark or Databricks; GPU acceleration (NVIDIA CUDA) supports large-scale stress testing. Regulatory reporting pipelines use Snowflake or Azure Synapse with dbt transformations. Workflow orchestration uses Airflow or Prefect, with Grafana for real-time risk dashboards and OpenFin for trading-terminal embedding.

Market Context

The financial risk analytics market is dominated by BlackRock Aladdin, which covers over USD 20 trillion in assets, alongside MSCI RiskManager, S&P Global Financial Risk Analytics, Moody's Analytics RiskAuthority, and SAS Risk Management. All major incumbents operate on enterprise commercial subscriptions; mid-market institutions are systematically underserved. Primary buyers are risk managers, CROs, and compliance functions at regional banks, mid-sized asset managers, hedge funds, and insurance companies.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.