Insurance Renewal Assessment Digital Worker
The Insurance Renewal Assessment Digital Worker orchestrates a multi-agent AI system that automates the entire renewal evaluation process. Eight specialized AI agents work collaboratively through a four-phase workflow: Data Intake & Validation, Multi-Agent Analysis, Pricing Optimization, and Recommendation Synthesis.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agent Workflow Live Execution
Agent Reasoning and Evidence Chain
Risk Assessment and Pricing Analysis
Executive Summary and Final Decision
AI Agents
Specialized autonomous agents working in coordination
Workflow Orchestrator Agent
Complex multi-agent workflows require centralized coordination to manage task dependencies, handle agent handoffs, resolve conflicts between agent recommendations, and synthesize outputs into coherent decisions. Without orchestration, agents work in isolation producing fragmented, inconsistent results.
Core Logic
The Workflow Orchestrator coordinates the execution of all specialized agents through a structured four-phase workflow. It manages data flow between agents, routes tasks based on dependencies, resolves conflicting recommendations through weighted consensus, maintains comprehensive audit trails of all decisions, and ensures workflow completion within defined SLAs. The orchestrator uses GPT-4o with low temperature for deterministic coordination.
Data Extraction Agent
Renewal assessments require data from multiple sources including uploaded documents, policy databases, and demographic systems. Manual data extraction is slow, error-prone, and fails to identify missing or inconsistent information across sources.
Core Logic
The Data Extraction Agent uses GPT-4o-mini to parse uploaded policy documents, extract structured data from certificates and census files, validate data completeness against required fields, cross-reference with policy database records, normalize date formats and currencies, and flag missing or inconsistent information. It employs Document Intelligence tools for OCR processing and produces data quality reports for downstream agents.
Risk Analysis Agent
Insurance risk assessment requires evaluating multiple risk dimensions including financial stability, operational factors, demographic profiles, and industry-specific risks. Traditional actuarial methods cannot process the volume and variety of signals available in modern data environments.
Core Logic
The Risk Analysis Agent leverages Claude 3.5 Sonnet for advanced reasoning to evaluate multi-dimensional risk factors. It executes proprietary risk scoring ML models, compares metrics against industry benchmarks, identifies risk concentration areas within portfolios, calculates weighted risk scores across age, claims, and operational dimensions, and generates risk mitigation strategies with confidence scores. The agent performs trend analysis to detect deteriorating risk profiles.
Claims Intelligence Agent
Claims history contains critical signals for renewal pricing but analyzing patterns, trends, seasonality, and cost drivers across thousands of claims records manually is impractical. Underwriters often miss important patterns that impact future claims projections.
Core Logic
The Claims Intelligence Agent uses GPT-4o to analyze 12-month claims history, detecting seasonal patterns using time-series analysis. It identifies top cost drivers, calculates claim severity trends, forecasts future claims using statistical models, computes loss ratio trajectories, and prepares comprehensive claims intelligence reports. The agent uses semantic search to find relevant historical precedents for unusual claim patterns.
Fraud Detection Agent
Insurance fraud costs the industry billions annually through coordinated billing schemes, duplicate claims, diagnosis-claim mismatches, and suspicious provider networks. Traditional rule-based systems miss sophisticated fraud patterns that evolve to evade detection.
Core Logic
The Fraud Detection Agent employs Claude 3.5 Sonnet with ML-powered fraud pattern detection to scan claims for known fraud indicators, analyze provider billing patterns for anomalies, detect duplicate claim submissions, validate claim-diagnosis consistency against medical protocols, run anomaly detection models, and generate fraud risk scores with investigation recommendations. The agent screens entities against sanction lists for compliance.
Pricing Optimization Agent
Optimal renewal pricing must balance profitability, competitiveness, and retention while incorporating risk assessments, claims forecasts, and market conditions. Manual pricing often relies on simple adjustments that fail to capture the full picture.
Core Logic
The Pricing Optimization Agent uses GPT-4o to apply actuarial pricing models incorporating risk-adjusted factors from other agents. It models wellness program impacts on claims, fetches competitor pricing intelligence for market positioning, generates multiple pricing scenarios (conservative, balanced, aggressive), computes agent consensus scores across scenarios, and produces pricing recommendations with detailed rationale and competitive analysis.
Compliance Check Agent
Insurance operations must comply with complex regulatory requirements including KYC verification, sanction screening, IRDAI regulations, and policy term compliance. Manual compliance checking is time-consuming and risks missing violations that could result in penalties.
Core Logic
The Compliance Check Agent uses Claude 3.5 Haiku for fast, accurate compliance validation. It initiates KYC verification against official registries, validates company registration status, screens entities against global sanction and PEP lists, checks IRDAI compliance requirements for policy terms, validates premium calculation methodology against regulations, and generates compliance certificates with full documentation for audits.
Recommendation Synthesis Agent
Multi-agent analysis produces diverse findings and recommendations that may conflict or require prioritization. Decision-makers need a unified view that synthesizes all inputs into clear, actionable recommendations with supporting evidence and confidence levels.
Core Logic
The Recommendation Synthesis Agent uses GPT-4o to aggregate findings from all agents, weight and prioritize recommendations based on impact and confidence, resolve conflicting assessments through evidence evaluation, generate clear actionable recommendations with executive summaries, prepare comprehensive audit trail documentation, and produce final renewal decisions (approve, approve-with-conditions, refer-to-underwriter, decline) with detailed rationale.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization