AI Simulation Engine: Validate your AI forecasts with real historical data
The deployment of AI in business processes is becoming increasingly advanced, 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 allows organizations to validate their forecasts against historical data. This way, you know in advance whether your AI models are ready for practical application.
Applications for banks, insurers, and energy companies
- Banks can deploy the AI Simulation Engine to more accurately calculate mortgage risks. By running simulations on historical mortgage data, supplemented with external factors, banks can substantiate their risk assessment and interest rates with hard figures.
- Insurers gain insight into risks within existing coverages as well as the effect of new policy conditions using the simulation engine. By running simulations on 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 accurately forecasting energy demand. They must not only match supply to demand in the short term but also purchase energy and plan production capacity in the long term based on expected developments. Reliable forecasting models are crucial for this. With the AI Simulation Engine, energy companies can calculate various scenarios, utilizing 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 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.
Companies can use AI to build a digital twin of their organization, allowing them to digitally simulate future business changes before implementing them in reality. Also read our extensive article on Digital Twins and AI Strategy for more background information.
Transparency and reliability as a foundation
The unique aspect of this approach: the simulation engine makes forecasts transparent and demonstrably reliable. By comparing predictions based on historical data with actual realized results, organizations can objectively assess and specifically improve the predictive power of their AI model. In a stock market case, for example, it immediately shows how closely a model approaches reality — and only when 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 custom work, where we determine together with you which data, scenarios, and validations are most relevant. This can be in the form of consultancy or based on a fixed price, depending on your wishes 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?
Get in touch for a non-binding demo or more information.
External references:
Backtesting: Definition, How It Works
What is a Digital Twin
