Insurance transformation via AdInsure ML
AdInsure provides a full suite of tools for data-driven automation of core insurance processes. Benefit from data mining, robust machine learning infrastructure, and comprehensive visualization tools. AdInsure's open architecture allows our partners and clients to seamlessly build and integrate ML models, enhancing insurance workflows with flexibility and advanced data capabilities.
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Empowering ML: AdInsure's AI Infrastructure
AdInsure offers the necessary infrastructure to empower machine learning within the insurance sector.
It includes an optional suite for data mining and machine learning, complete with a data warehouse, machine learning servers, and advanced visualization tools. This facilitates the development and application of machine learning models, enabling their seamless utilization within the AdInsure ecosystem.
Seamless Integration of AI/ML into core insurance processes
Leveraging its open architecture, AdInsure seamlessly integrates AI/ML models into core insurance processes. Our data science team has crafted specific models for crucial sales and claims areas, ensuring efficient and effective deployment within AdInsure’s flexible and modern framework.
Exploring AdInsure AI/ML Use Cases
Anomaly detection on a claim calculation amount
- Detects if humanly provided calculation amounts are out of the ordinary by comparing them with historical data
- Calculation amounts are categorized by their Coverage and Risk
- Estimation of the expected amount
- Seamlessly integrated into the AdInsure supported Claims process
Data Point Grouping for Insightful Analysis
- Grouping of data points, across user-defined clusters for detailed analysis and visualization
- Example: usage of Past Premium and Claim Data to optimize agent performance
- Support Dynamic Clustering Analysis by leveraging AdInsure Data Warehouse integration
Enhancing Sales Process with Advanced Recommender Systems
- Implicit Recommender Systems: leverage feedback data for agent-specific policy recommendations
- Service Provider Selection: Recommending medical service providers based on claimant history
- Optimizing Claim Handlers: Suggesting claim handlers by balancing their past performance with current availability
