Understanding risk is a fundamental part of business and government. We have the tools and expertise to help you get the insights you need.
Using your data with our database and modelling technologies and rigorous scientific approach, we deliver clearer insights which underpin important decisions for your business or organisation.
Risk analysis is the process of identifying and analyzing potential issues that could negatively impact key business initiatives or critical projects in order to help organizations avoid or mitigate those risks. It is a very broad area which includes risks as diverse as:
- Financial loss due to an economic downturn, or bankruptcy of customer or supplier.
- Supply chain quality or contamination risk
- Damage by fire, flood or other natural disasters.
- Loss of important suppliers or customers.
- A decrease in market share because of new competitors in the market
- Population or worker health effects from chemical exposure
- Fraud in insurance or financial businesses
- Many more…
Risk is uncertain by its nature but can be quantified and understood using data and scientific modelling.
Quantitative Risk Modelling
Our Data Foundry and Expert Models platforms create the basis for delivering advanced quantitative risk analysis. We implement commercial-grade risk models for industry and deliver them using a user-friendly web-based interface. This takes your risk analysis out of Excel and into a robust probabilistic modelling framework using the latest computing and programming technologies.
In many scenarios, assumptions must be made and estimates computed. Coming up with a single point estimate for key inputs can be challenging and misleading. It is more realistic and accurate to give a range. For example, a minimum, maximum and most likely value. Typically, we consult a group of experts on missing data and come to a consensus on the range and magnitude of these values
Once agreed, missing data can be represented by a probabilistic expression and that information is then propagated through our model using a probabilistic modelling technique, such as Monte Carlo simulation.
In Monte Carlo simulation, inputs are entered as data ranges of possible values to represent variability and/or uncertainty, and the model is iterated to explore the problem space and compute the risk. Typically, the greater the number of variables and input ranges the greater the number of iterations required for the simulation to converge.
At Creme Global, we have deep expertise in running large calculations such as these on our scalable cloud-based platform, Expert Models.
- Business analysis and risk review
- Review of data and identification of new data sources
- Missing data imputation through expert elicitation
- Data modelling and database design and connection
- Data collection portals
- Development of new risk score metrics
- New risk model prototype development and testing
- Deployment of prototype and finalised models on our Expert Models platform
- Document all assumptions in the report
- Risk analysis and final reporting