Board / CEO
Controllability across growth, profitability, and risk with clear KPI signals.
KAITUM AI for insurance
AI decision intelligence for underwriting, sales, claims, and compliance
For insurers under high decision pressure: KAITUM connects data, prioritizes action options, and makes operational decisions transparently controllable across functions.
UnderwritingClaimsSalesFraudCompliance
Executive cockpit
Combined ratio, time-to-quote, fraud hit rate, and churn in one decision flow.
Not just reporting: clear next actions per function.
For decision-makers
What each leadership function concretely gains
CRO / Underwriting leadership
More precise risk decisions with shorter time-to-quote and higher transparency.
Sales board / Sales leadership
Higher conversion through prioritized leads, next best offer, and cross-channel orchestration.
COO / Operations
Higher straight-through processing in policy, service, and claims processes.
Claims leadership / SIU
Faster settlement while detecting suspicious fraud patterns earlier.
Compliance & Internal audit
End-to-end audit trails and reliable documentation for regulatory requirements.
Board-level relevance
Growth, risk, and efficiency stop being trade-offs when decisions are orchestrated data-first.
Value levers
9 AI levers across the insurance value chain
Risk assessment & underwriting
Heterogeneous data sources, AI scoring, and more precise risk decisions.
Outcome: Higher close rates with lower loss ratios.
Sales & distribution
Offer fusion, lead prioritization, and context-sensitive sales interaction.
Outcome: More cross-/upsell and shorter sales cycles.
Customer service & claims handling
Automated claims classification, self-service, and early review of suspicious cases.
Outcome: Faster reimbursement with lower process costs.
Data integration & 360° customer profile
Central data hub across CRM, policy, claims, and external market signals.
Outcome: Better segmentation and more targeted product decisions.
Process automation (RPA + AI)
Automation of repetitive back-office processes incl. compliance checks.
Outcome: Faster cycle times and fewer manual errors.
Retention & personalization
Life-event-based communication and renewal/premium optimization.
Outcome: Higher loyalty and lower churn rates.
Product development & pricing
Dynamic pricing, scenario simulation, and margin steering.
Outcome: More competitive tariffs with more stable margins.
Regulatory compliance & reporting
Automated documentation and impact analysis of regulatory changes.
Outcome: Lower regulatory risk and reduced manual effort.
Fraud detection & security
Anomaly detection across claims, policy, and behavioral patterns.
Outcome: Lower fraud costs and better prioritization for investigation teams.
Operating model
How decision-makers anchor AI effectively in legacy environments
1. Data fabric
An integrated data layer connects policy, claims, CRM, partner data, and external signals.
2. Decision engines
AI models for underwriting, next best offer, fraud risk, and churn prevention.
3. Workflow orchestration
Role-based next actions for sales, underwriters, claims, and compliance.
4. Governance & audit
Transparent decision logic, monitoring, and robust audit trails.
KPI cockpit
Measurable impact for board and leadership teams
Time to quote
faster
Combined ratio
more stable
Fraud hit rate
higher
STP rate
significantly higher
Claims cycle time
shorter
Renewal rate
higher
Cross-/upsell rate
increasable
Audit effort
lower
3-step plan
From pilot to scalable insurance AI
Step 1
Use-case prioritization
KPI-based prioritization by business value and feasibility in legacy systems.
Step 2
Pilot on critical process
Start with one decision-critical process (e.g., underwriting or claims triage).
Step 3
Scale & governance
Rollout into adjacent teams with clear governance, monitoring, and reporting standards.