AI Simulation Engine: Validate Your AI Forecasts with Real Historical Data
The use of AI in business processes is becoming increasingly sophisticated, but how can you be sure that your AI models are making truly reliable predictions? Fortis AI introduces the AI Simulation Engine: a powerful approach that enables organizations to validate their forecasts against historical data. This allows you to determine in advance whether your AI models are ready for real-world use.
Applications for Banks, Insurers, and Energy Companies
- Banks can use the AI Simulation Engine to calculate mortgage risks more accurately. By running simulations on historical mortgage data supplemented with external factors, banks can substantiate their risk assessments and interest rates with hard figures.
- Insurers gain insight with the simulation engine into both risks within existing coverage and the impact of new policy terms. By running simulations on their claims records, they can calculate the impact of changes in advance and thereby optimize their claims portfolio.
- Energy companies Face the challenge of accurately forecasting energy demand every day. They must not only align supply with demand in the short term, but also procure energy over the longer term and plan production capacity based on expected developments. Reliable forecasting models are crucial in this process. With the AI Simulation Engine, energy companies can calculate various scenarios using both internal consumption data and external factors such as weather forecasts, market prices, and policy developments. This provides insight into the reliability of models and enables better-informed strategic decisions.
A Digital Twin as a Powerful Tool
The AI Simulation Engine fits within the broader Fortis AI vision:
Train, Simulate, Analyze, Retrain, Operate.
Companies can use AI to build a digital twin of their organization, enabling them to digitally simulate future business changes before implementing them in practice. Also read our in-depth article on Digital Twins and AI Strategy for more background.
Transparency and Reliability as the Foundation
What makes this approach unique is that the simulation engine makes forecasts transparent and demonstrably reliable. By comparing predictions based on historical data with the results actually achieved, organizations can objectively assess and systematically improve the predictive capabilities of their AI model. In a stock market case, for example, it immediately becomes clear how closely a model reflects reality—and only once the margin of error is acceptably small (for example, <2%) is the model ready for operational deployment.
Building Reliable AI Together
The AI Simulation Engine is always tailored to your specific business case and data. Fortis AI delivers this solution as a customized service, working with you to determine which data, scenarios, and validation methods are most relevant. This can be provided as consultancy or on a fixed-price basis, depending on your requirements and the complexity of the assignment.
Want to Learn More or See a Demo?
Would you like to know what the AI Simulation Engine could mean for your organization? Or would you like to discuss the possibilities for your specific industry?
Contact us for a complimentary demo or more information.
External References:
Backtesting: Definition: How It Works
What is a Digital Twin
