OUR ANALYTICS & MODELLING SOLUTIONS
Tackle the world’s most complex risks & get insights to optimise your portfolio
Our cutting-edge and scientifically rigorous models quantify the portfolio effects of primary and secondary hazards, cascading effects across systems, and agent-based decisions. This provides a holistic analysis of risks that allow you to make high-quality risk-informed decisions, including:
An optimised portfolio through risk insights & managing risks according to your strategies
Improved decision-making by ensuring decisions incorporate overlooked cascading systemic risks
Exposure management by being prepared for high-risk grey-swan scenarios
Customers & Key Benefits
Portfolio allocation: Quantify and reduce the direct and indirect impacts from CPC risks
Portfolio stress testing: Run through scenarios to simulate effects on your portfolio
Corporate guidance: Guide corporations to bettter manage CPC risks
Portfolio allocation: Incorporate CPC risks into portfolio and reinsurance decisions
Underwriting: Improve underwriting guidelines, and integrate risks into pricing
Stress Testing: Apply stress testing as part of risk and solvency assessment processes
Central Banks, Governments, & Financial Regulators
Systemic Framework: Incorporate the cascading and systemic impacts in stress testing frameworks
Stress Testing: Probe the resilience of the financial system and assess how the system can cope with severe and plausible scenarios
We model primary and secondary hazards & threats, first-order and second-order system impacts, agent responses, and interlinkages. We use methods and simulations with a wide range of economic and process-based models, input-output models, agent-based models, cross-sectoral analysis, network analysis, policy analysis, and statistical analysis of existing datasets.
We use historical data and consult with academics, and forecasters to calibrate distributions and probabilities. We also use poll survey data and consult with governments to gain insight into agent-based decisions.
Our models differ from others in that we quantify interlinkages often neglected in other models, such as commodity price inflation and civil unrest, and trade and supply chain impacts, ranging from raw materials, production, processing and distribution. Quantifying these interlinkages allows us to overcome omitted variable bias.
1) Primary and Secondary Hazard Profiles & Agent-based Responses
Using data climate and environmental data, and socioeconomic data, the frequency and intensity of primary and secondary hazard profiles are mapped at the country level. Several of the systemic impacts are contingent on responses by governments, households, firms and other actors. We use agent-based models with observation-informed assumptions and geography-based localisations.
Using public and private data, the probability that a certain loss value will be exceeded due to the primary event is calculated and graphed for a predefined amount of time in the future.
2) Exceedance Probability Data
3) Monte Carlo Simulation of Outcomes
Monte Carlo simulations and sensitivity analysis are run to predict the impacts on sovereign debt default risk, expected loss and macroeconomic variables (i.e. GDP, interest rate, exchange rate, balance of payments, government debt, government spending, tax rate).
Our Software Platform
Key Software Features
Dynamic dashboard showing cVAR and tVAR for a set of climate related natural hazards, and emerging infectious disease profiles
Monte Carlo simulations and sensitivity analysis to illustrate how outcomes change with input value changes
API for seamless integration with portfolio analysis tools
Investment evaluations for resilient financial productions, including how they reduce portfolio cVAR