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 certain that your AI models are making genuinely reliable predictions? NetCare introduces the AI Simulation Engine: a powerful approach that allows organizations to validate their forecasts using historical data. This lets you know in advance whether your AI models are ready for practical deployment.
Applications for banks, insurers, and energy companies
- Banks can deploy the AI Simulation Engine to calculate mortgage risks more accurately. By running simulations on historical mortgage data, supplemented by external factors, banks can substantiate their risk assessment and interest rates with hard figures.
- Insurers gain insight into both risks within existing coverage and 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 their claims portfolio.
- Energy companies face the daily challenge of accurately predicting energy demand. 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 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 allows for better-substantiated strategic decisions.
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 build a digital twin of their organization, enabling them to digitally simulate future business changes before implementing them in reality. Read our detailed article on Digital Twins and AI Strategy for more background information.
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 actually realized results, organizations can objectively assess and specifically improve the predictive capability of their AI model. In a stock market case, for instance, it immediately becomes clear how closely a model approaches reality — and only when the error margin 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. NetCare delivers this solution as a custom service, working with you to determine which data, scenarios, and validations are most relevant. This can be provided in the form of consultancy or on a fixed-price basis, depending on your requirements 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
