Quantica Risk Modelling is a concept for an AI-native, explainable risk platform. It combines calibrated statistical models, machine learning components, structured expert judgement and regulator-grade governance into one modelling environment. The design principle is simple: every number that reaches a board pack or a supervisory filing can be traced back to data, methodology and validation evidence.
Credit risk under Basel III, IFRS 9 expected credit loss, PD term structure modelling, LGD and collateral valuation, IRRBB and ALM, ICAAP capital planning, ILAAP liquidity stress testing, concentration risk, and integrated climate credit risk overlays for mortgage, corporate and SME portfolios.
Solvency II pillar 1 and pillar 2 modelling, ORSA scenario design, reserving under IFRS 17, underwriting risk, catastrophe modelling, reinsurance structure evaluation, asset-liability integration, and climate physical and transition overlays for property, motor, health, life and specialty lines.
Hazard, exposure, vulnerability and loss engines aligned with NGFS scenarios and bespoke pathways. Physical perils (flood, wind, heat, wildfire, drought, coastal) are modelled to loss, and transition pathways translate into sector-level shocks flowing through PD, LGD, valuation and reserves.
Fixed income, equity, real estate, infrastructure and structured credit are re-priced under coherent macro-financial shocks. Scenarios respect no-arbitrage constraints, hedge relationships and liquidity segmentation so results are usable for capital planning, not just headline numbers.
Independent validation is built into the workflow: model inventory, documentation, backtesting, benchmarking, sensitivity analysis, expert challenge, limitations, findings, remediation and approval. Change control and re-validation triggers are explicit, not implicit.
Calibration is data-driven, not held together by undocumented fixed assumptions. Public hazard data, macroeconomic series, sector datasets, market data and internal exposure data are ingested through a governed pipeline. Fitted parameters, expert overrides and uncertainty ranges are all documented.
Designed with Solvency II, Basel III, IFRS 9, IFRS 17, ICAAP, ILAAP and ORSA in mind. Supervisory climate stress tests, EBA guidelines and NGFS scenario applications are treated as first-class outputs, not afterthoughts bolted on to a pricing tool.
Modular engines (hazard, exposure, vulnerability, loss, scenario, financial-impact, validation, reporting) communicate through a typed contract. Machine-learning components are wrapped with explainability, monitoring and human review. Everything is version-controlled, tested and reproducible.
Quantica is a working concept, iterated with practitioners in banking, insurance, reinsurance and asset management. If you're scoping an AI-native risk modelling, climate risk or model validation programme, I'm open to conversations under NDA.