Health Risk Scoring Agent
Generates dynamic, explainable health risk scores for personalized and fair underwriting decisions
Description
Challenge:
Health underwriting requires balancing speed, fairness, and risk accuracy. Traditional methods rely heavily on manual review, fixed tables, and subjective judgment, leading to inefficiencies, pricing inconsistencies, and missed risks. As applicant health data grows more complex—covering diagnostics, lifestyle, wearables, and claims—insurers need a predictive, scalable way to quantify risk.
How It Works:
The Health Risk Scoring Agent ingests applicant health, lifestyle, and claims data to generate predictive risk scores using machine learning models and actuarial frameworks. It evaluates disclosures, lab results, medical history, and lifestyle habits, applies risk factor weights and comorbidity models, and classifies applicants into risk bands. The score highlights key risk drivers and explains underlying contributors, ensuring transparent and auditable decision support. The output integrates directly with underwriting engines to trigger eligibility rules, pricing alignment, and additional test requirements, enabling fairer, faster, and more accurate underwriting.
Benefits
85–90% accuracy in health risk banding (validated against historical claims)
60–70% of low-risk applicants fast-tracked without manual underwriting
45% reduction in manual review for standard profiles
30% improvement in premium pricing alignment with actual risk
100% explainable scoring with transparent risk factor attribution
Ensures consistent, fair, and bias-free assessment across all applicants
Resources
Features
This agent consolidates medical, lifestyle, and claims data into a unified scoring framework, providing actionable health risk insights for underwriting and pricing decisions.
Features & Capabilities:
Data Ingestion: Captures disclosures, diagnostics, lifestyle inputs, and prior claims.
Feature Extraction: Derives critical parameters such as age, BMI, blood pressure, HbA1c, smoking status.
Dynamic Risk Scoring: Applies ML/actuarial models to compute scores on a 0–100 scale.
Risk Band Classification: Categorizes applicants into low, moderate, or high-risk groups.
Threshold Rules Application: Auto-approves low risk, routes moderate cases for testing, flags high risk.
Lifestyle Adjustment: Penalizes smoking, obesity, alcohol use, or sedentary behavior.
Claims & Chronic Condition Penalties: Adjusts scores for recent claims or long-term illnesses.
Reinsurer Alignment: Routes cases exceeding global thresholds for manual referral.
Explainability: Highlights top contributing risk factors for transparent decisioning.
Seamless Integration: Feeds scores to underwriting engines, pricing systems, and rule engines.
Operating Blueprint
Knowledge Sources:
Application Forms & Questionnaires: Collects applicant demographics, lifestyle details, and medical disclosures.
Medical Records & Clinical Summaries: Includes hospital records, lab results, and diagnostic reports for health profiling.
Diagnostic & Laboratory Data: Uses structured lab outputs and pathology findings for clinical validation.
Underwriting Guidelines & Rules: Refers to insurer-specific manuals and industry-standard rules.
Historical Underwriting & Claims Data: Leverages past underwriting cases and claim outcomes for predictive accuracy.
Regulatory & Compliance Frameworks: Ensures assessments meet data privacy and insurance compliance standards.
Business Rules:
Data Completeness Rule: Score generated only if minimum health data is available.
Risk Scoring Thresholds: Scores above 80 fast-track approval, 50–80 trigger extra medical tests, and below 50 route applicants for detailed review.
Lifestyle Penalty Rule: Apply deductions for smoking, obesity, sedentary lifestyle.
Claims History Rule: Penalize frequent or high-value past claims.
Chronic Condition Rule: Apply deductions for long-term diseases (diabetes, hypertension).
Reinsurer Referral Rule: Escalate when reinsurer-defined thresholds are exceeded."
Tool Workflow:
Receive health disclosures, diagnostic results, lifestyle inputs, and claims history.
Extract features (age, BMI, vitals, conditions, medications).
Process data through predictive health risk models.
Calculate risk score on a standardized 0–100 scale.
Classify into risk band (low, moderate, high).
Apply business rules (thresholds, penalties, referrals).
Generate explainable scorecard with key risk drivers.
Pass score to underwriting engine for rule-based decisioning.
Trigger auto-decisioning, medical tests, or manual review based on score.
Badges
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Capabilities
