01 · RISK & UNDERWRITING
Risk Assessment
- Health-risk scoring
- Medical questionnaire auto-evaluation
- Chronic-disease prediction
- Long-term care risk forecasting
The intelligence engine behind smarter insurance decisions.
GuardianAI is a modular, API-ready platform for underwriting decision support, health-risk scoring, claims intelligence, pricing, long-term care forecasting and reinsurance analytics.
Technology foundation
Localized data, modular models and explainable outputs.
The underlying documentation describes a longitudinal data foundation and a model suite developed for health and insurance use cases, with particular focus on Asian populations.
30B+documented data points
23M+lives represented
15+years of history
97.3%reported accuracy in specified Asian-population validation
These figures relate to underlying technology and model documentation, not every deployment. Results vary and are not guaranteed.
What it supports
Decision support across the insurance workflow.
Underwriting
Pricing
Claims triage
Reinsurance modelling
Health engagement
API integration
Modular capabilities
Deploy the modules that fit the use case.
Modules are configured and validated for the client’s population, workflow, authority and governance requirements.
02 · PRICING & PRODUCT
Pricing Intelligence
- Dynamic pricing support
- Risk segmentation
- Product and contribution analytics
- Lifestyle and wellness adjustments
03 · CLAIMS & COST
Claims Intelligence
- High-cost claims forecasting
- Claims triage support
- Pattern and fraud signals
- Chronic-disease deterioration monitoring
04 · RISK POOLING
Reinsurance Analytics
- Risk-pool stratification
- Expected-loss curve generation
- Tail-risk forecasting
- Portfolio and capital-efficiency analytics
Privacy-preserving architecture
Designed to move intelligence, not unnecessary source data.
The documented architecture uses controlled training environments and can incorporate privacy-preserving methods according to project needs.
Federated Learning
Models can be trained within approved data environments, with permitted model updates used outside the source environment.
Synthetic Data
Statistically representative synthetic data can support model development without containing real individual records.
Differential Privacy
Noise mechanisms can reduce the risk of inferring information about an individual from model outputs.
The specific privacy, security and compliance controls depend on the deployment, data controller, jurisdiction and agreed technical architecture.
Commercial model
Flexible paths to licensing and integration.
Module Licensing
Annual licensing for selected GuardianAI modules, subject to scope, market and contractual terms.
Usage-Based APIs
API access structured around authorised calls, transactions or defined operational usage.
Implementation
Integration, configuration, model tuning, validation and workflow design as a scoped project.
Responsible deployment
Human review remains part of the control framework.
GuardianAI is a decision-support platform. It does not provide medical advice and does not independently issue, bind, accept or reject insurance coverage.
- Final decisions remain with authorised professionals
- Model validation is specific to population and use case
- Outputs should be monitored for drift, bias and data-quality issues
- Deployment is subject to applicable privacy, insurance and technology requirements
Evaluate GuardianAI against a defined workflow.
Begin with a scoped proof of concept for underwriting, claims, pricing, LTC or risk analytics.