AI Simulation Engine: Validate your AI forecasts with real historical data
The use of AI in business processes is becoming increasingly advanced, but how can you be sure your AI models are truly making reliable predictions? Fortis AI introduces the AI Simulation Engine: a powerful approach that enables organizations to validate their forecasts using historical data. This way you know 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 support their risk assessments and interest rates with hard data.
- Insurers gain insight with the simulation engine into both risks within existing coverages and the effect of new policy conditions. By simulating their claims administration they can calculate the impact of changes in advance and thus optimize the claims portfolio.
- Energy companies face the daily challenge of forecasting energy demand accurately. They must not only match supply to demand in the short term, but also procure energy and plan production capacity for the longer term based on expected developments. Reliable forecasting models are crucial for this. With the AI Simulation Engine, energy companies can run through different scenarios using both internal consumption data and external factors such as weather forecasts, market prices and policy developments. This provides insight into model reliability and allows strategic decisions to be better substantiated.
A digital twin as a powerful tool
The AI Simulation Engine fits within the broader Fortis AI vision:
Train, Simulate, Analyze, Retrain, Operate.
With AI, companies can build a digital twin of their organization, and thus digitally simulate future business changes before implementing them in reality. Also read our in-depth article on Digital Twins and AI Strategy for more background.
Transparency and reliability as a 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 actual realized outcomes, organizations can objectively assess and improve the predictive power of their AI model. In, for example, a stock-case, it becomes immediately clear how closely a model approximates reality — and only when the error margin is acceptably small (for example <2%) is the model ready for operational deployment.
Building trustworthy AI together
The AI Simulation Engine is always tailored to your specific business case and data. Fortis AI delivers this solution as a custom implementation, where we determine together which data, scenarios, and validations are most relevant. This can be provided as consultancy or on a fixed-price basis, depending on your needs and the complexity of the assignment.
Want to know more or see a demo?
Would you like to know what the AI Simulation Engine can do for your organization? Or would you like to discuss the possibilities for your specific industry?
Contact us for a non-binding demo or more information.
External references:
Backtesting: Definition, How It Works
What is a Digital Twin
