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 are making truly reliable predictions? NetCare introduces the AI Simulation Engine: a powerful approach that allows organizations to validate their forecasts against historical data. This way, you know in advance if your AI models are ready for practical application.

Applications for Banks, Insurers, and Energy Companies

• Banks can use 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 assessments and interest rates with hard figures.
• Insurers gain insight into risks within existing coverage and the impact of new policy conditions with the simulation engine. By running simulations on their claims administration, they can calculate the impact of changes in advance and thus optimize their claims portfolio.
• Energy companies face the daily challenge of accurately predicting energy demand. They must not only match supply with 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, utilizing both internal consumption data and external factors such as weather forecasts, market prices, and policy developments. This provides insight into model reliability and allows for better-informed strategic decisions.

A Digital Twin as a Powerful Tool

The AI Simulation Engine aligns with the broader NetCare vision:
Train, Simulate, Analyze, Retrain, Operate.
Companies can build a digital twin of their organization with AI, allowing them to digitally simulate future business changes before implementing them in reality. Read our comprehensive article on Digital Twins and AI Strategy for more background.

Transparency and Reliability as a Foundation

What’s unique about 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 systematically improve the predictive power of their AI models. For example, in a stock market case, it immediately shows how closely a model approximates reality – and only when the margin of error 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 build, where we determine with you which data, scenarios, and validations are most relevant. This can be in the form of 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 no-obligation demo or more information.

External References:

Backtesting: Definition, How It Works

What is a Digital Twin