We use our expertise to combine classical models and approaches with IA methods.
We deliver solutions at a fast pace, not delaying go-to-market and we guarantee that our models can be audited.
Data Collection & Preprocessing: Gather and clean data from various sources like claims, financial records, and customer demographics.
Exploratory Data Analysis (EDA): Utilize statistical analysis and visualizations to uncover initial patterns and correlations.
Model Evaluation & Validation
Performance Metrics: Use MAE, RMSE, and R-squared to measure accuracy.
Cross-Validation: Test model reliability on various data subsets.
ML Models
Clustering: Group similar data points to reveal hidden patterns.
Regression Analysis: Predict future values based on historical data.
Time Series Analysis: Forecast trends using models like ARIMA and LSTM.
Classification Algorithms: Categorize data to identify specific trends.
Implementation & Monitoring
Continuous Improvement: Regularly update models with new data.
Deployment: Integrate AI models into actuarial systems for real-time monitoring.