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 that your AI models truly make reliable predictions? NetCare 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 deployment.

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 numbers.
• Insurers Gain insight with the simulation engine into both risks within existing coverages and the effect of new policy terms. By simulating their claims administration, they can pre-calculate the impact of changes and thereby optimize the loss 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 longer term based on expected developments. Reliable forecasting models are crucial for this. With the AI Simulation Engine, energy companies can run 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 allows strategic decisions to be better substantiated.

A digital twin as a powerful tool

The AI Simulation Engine fits within the broader NetCare vision:
Train, Simulate, Analyze, Retrain, Operate.
Companies can use AI to a digital twin build of their organization, and thus digitally simulate future business changes before implementing them in reality. Also read our extensive article about Digital Twins and AI strategy for more background.

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 deliberately improve the predictive capability of their AI model. For example, in a stock case, it becomes immediately clear how closely a model approximates reality — and only when the error margin is acceptably small (e.g., <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. NetCare delivers this solution as a custom offering, working with you to determine 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 project.

Want to know more or see a demo?

Would you like to know what the AI Simulation Engine can mean for your organization? Or would you like to discuss the possibilities for your specific industry?
Contact us for a no‑obligation demo or more information.

External references:

BacktestingDefinition, How It Works

What is a Digital Twin