Agentic Journeys for Insurance
Smarter, Faster Life Insurance Decisions with Predictive and Agentic AI
Empower operations with agentic AI to enable smarter underwriting and faster, more accurate claims. Leverage predictive insights for fraud detection, early claim assessment, and dynamic risk scoring—boosting precision, ensuring compliance, and aligning risk selection with future claim propensity for greater profitability and customer-centricity.
Key Features of Newgen’s Agentic Journeys for Life Insurance
Dynamic Risk Scoring for Enhanced Underwriting Precision
Utilize AI models that assess customer demographics, financial health, disclosures, and behavioral indicators to assign a dynamic risk score during underwriting. These scores evolve as new information is captured across the policy lifecycle, allowing underwriters to fine-tune decisions and maintain a real-time risk view. Improve profitability by aligning premium pricing with future claim propensity.
Early Claim Probability Model for Underwriting Triage
Predict the likelihood of early claims (within 2–3 policy years) at the underwriting stage using historical claims, morbidity indicators, income consistency, occupation volatility, and lifestyle attributes. This model helps underwriters identify high-risk applicants and initiate enhanced scrutiny, document validation, or dynamic questionnaire routing, mitigating adverse selection and improving long-term portfolio health.
AI-driven Claims Fraud Detection & Pattern Recognition
Leverage historical claims patterns, policyholder behavior, medical histories, and third-party data to detect anomalies in claim submissions. The model assigns fraud likelihood scores and routes suspicious claims to specialized investigation paths. By combining machine learning with rule-based systems, insurers can flag high-risk claims early, reduce leakage, and safeguard regulatory compliance.
Unified Fraud Case Management for Life Claims
Manage flagged claims through an integrated environment enabling rule management, predictive fraud scoring, manual investigation workflows, and audit trails. This ensures timely resolution, proper documentation, and feedback loops to refine fraud detection models continuously, ensuring real-time adaptation to evolving fraud patterns and regulatory shifts.
Dynamic Risk Scoring for Enhanced Underwriting Precision
Utilize AI models that assess customer demographics, financial health, disclosures, and behavioral indicators to assign a dynamic risk score during underwriting. These scores evolve as new information is captured across the policy lifecycle, allowing underwriters to fine-tune decisions and maintain a real-time risk view. Improve profitability by aligning premium pricing with future claim propensity.
Early Claim Probability Model for Underwriting Triage
Predict the likelihood of early claims (within 2–3 policy years) at the underwriting stage using historical claims, morbidity indicators, income consistency, occupation volatility, and lifestyle attributes. This model helps underwriters identify high-risk applicants and initiate enhanced scrutiny, document validation, or dynamic questionnaire routing, mitigating adverse selection and improving long-term portfolio health.
AI-driven Claims Fraud Detection & Pattern Recognition
Leverage historical claims patterns, policyholder behavior, medical histories, and third-party data to detect anomalies in claim submissions. The model assigns fraud likelihood scores and routes suspicious claims to specialized investigation paths. By combining machine learning with rule-based systems, insurers can flag high-risk claims early, reduce leakage, and safeguard regulatory compliance.
Unified Fraud Case Management for Life Claims
Manage flagged claims through an integrated environment enabling rule management, predictive fraud scoring, manual investigation workflows, and audit trails. This ensures timely resolution, proper documentation, and feedback loops to refine fraud detection models continuously, ensuring real-time adaptation to evolving fraud patterns and regulatory shifts.
Built-in AI models for Life Insurers
Early Claim Probability Detection Model
Evaluate underwriting data like income volatility, occupation risk, and policyholder disclosure patterns to predict early mortality/morbidity claims. The model flags applications likely to claim within the first few years, helping underwriters apply tighter scrutiny or impose conditions for better loss control.
Risk Scoring for Dynamic Underwriting Model
Generate real-time risk scores based on applicant disclosures, lifestyle indicators, and socio-economic data during policy issuance. These scores guide the underwriter’s decision to issue standard, sub-standard, or refer for manual review—optimizing risk selection and minimizing subjectivity.
Fraud Pattern Analysis Model for Claims
Analyze claim filing behavior, historical discrepancies, and external red-flag signals (e.g., duplicate beneficiaries, sudden policy upgrades before death) to detect potential fraud. The agent flags high-risk submissions for deeper investigation, preventing payouts on fraudulent claims.
Predictive Escalation Mode for High-risk Claim Profiles
Use agentic models to automatically escalate claims with mismatches across policy data, medical records, or behavioral red flags. It helps triage and prioritize complex claims for manual review while ensuring fast-track processing for low-risk cases.
Early Claim Probability Detection Model
Evaluate underwriting data like income volatility, occupation risk, and policyholder disclosure patterns to predict early mortality/morbidity claims. The model flags applications likely to claim within the first few years, helping underwriters apply tighter scrutiny or impose conditions for better loss control.
Risk Scoring for Dynamic Underwriting Model
Generate real-time risk scores based on applicant disclosures, lifestyle indicators, and socio-economic data during policy issuance. These scores guide the underwriter’s decision to issue standard, sub-standard, or refer for manual review—optimizing risk selection and minimizing subjectivity.
Fraud Pattern Analysis Model for Claims
Analyze claim filing behavior, historical discrepancies, and external red-flag signals (e.g., duplicate beneficiaries, sudden policy upgrades before death) to detect potential fraud. The agent flags high-risk submissions for deeper investigation, preventing payouts on fraudulent claims.
Predictive Escalation Mode for High-risk Claim Profiles
Use agentic models to automatically escalate claims with mismatches across policy data, medical records, or behavioral red flags. It helps triage and prioritize complex claims for manual review while ensuring fast-track processing for low-risk cases.
Newgen’s AI Powered Policy Servicing for Life Insurance
Life Insurers need an AI-powered, automated solution that extends across the entire journey for policy servicing
Learn what Newgen’s Life Insurance Solutions can do for you
Non-journey Use Cases for Insurance
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Discover how Newgen empowers life insurers with predictive models to enhance underwriting, mitigate fraud, and drive faster, smarter claims decisions.
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