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 genuinely reliable predictions? NetCare introduces the AI Simulation Engine: a powerful approach that enables organizations to validate their forecasts against historical data. This way, you can determine 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 substantiate their risk assessments and interest rates with hard figures.
- Insurers gain insight with the simulation engine into both risks within existing coverage and the impact of new policy terms. By running simulations on their claims data, they can calculate the impact of changes in advance and thereby optimize their claims portfolio.
- Energy Companies face the challenge of accurately forecasting energy demand every day. They must not only align supply with demand in the short term, but also purchase energy and plan production capacity over the longer term based on anticipated developments. Reliable forecasting models are crucial in this regard. With the AI Simulation Engine, energy companies can calculate different 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 enables better-informed strategic decisions.
A Digital Twin as a Powerful Tool
The AI Simulation Engine fits within NetCare’s broader vision:
Train, Simulate, Analyze, Retrain, Operate.
With AI, companies can build a digital twin of their organization, allowing them to digitally simulate future business changes before implementing them in reality. Also read our in-depth article about Digital Twins and AI Strategy for more background.
Transparency and Reliability as the 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 the results that were actually achieved, organizations can objectively assess and systematically improve the predictive power of their AI model. In a stock market case, for example, it immediately becomes clear how closely a model reflects 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. NetCare delivers this solution as a customized service, working with you to determine which data, scenarios, and validations are most relevant. This can take the form of consultancy or be delivered at a fixed price, depending on your needs and the complexity of the assignment.
Want to Learn More or See a Demo?
Would you like to know what the AI Simulation Engine could mean for your organization? Or would you like to discuss the possibilities for your specific industry?
Get in touch for a no-obligation demo or more information.
External References:
Backtesting: Definition and How It Works
What Is a Digital Twin
