Insurance Client UC1 Executive Summary

The Problem

Insurance Client processes 500K+ claims annually from unstructured policy documents (PDS, schedules, endorsements). Manual extraction causes:

Delays: Claims processing extended 2-4+ weeks
Rework: 30-40% of extractions require manual correction or re-entry
Errors: Poor data quality cascades downstream impacting customer experience

The Solution

Deploy an AI-powered extraction platform using Kyndryl's Agent Builder to automatically extract structured policy fields from unstructured documents with built-in quality validation and standardization.

What It Does:

Uploads policy documents (PDF, images) via browser interface
Extracts key fields: policy#, coverage type, effective date, beneficiaries, exclusions, limits, etc.
Scores quality per field (95%+ target accuracy)
Flags anomalies and low-confidence extractions
Routes to claims, underwriting, and data warehouse systems
Human team can validate and correct in real-time

Business Imperative

95%

Extraction Accuracy

Reliable downstream data

60%

Speed Improvement

Faster claims approval

80%

Error Reduction

Fewer downstream issues

99.5%

Platform Uptime

Always-on capability

Implementation Roadmap: Use Cases

Use Case 1: Policy Data Extraction

Status: PHASE 4 & 5 — ACTIVE EXECUTION

Problem: Manual policy extraction from unstructured documents (PDS, schedules, endorsements) causes delays and errors.

Solution: AI-powered 13-step extraction pipeline that:

Ingests 500K+ claims annually in multiple formats
Extracts structured fields: policy#, coverage types, effective dates, beneficiaries, exclusions, limits
Indexes clauses for fast retrieval and cross-referencing
Creates vector embeddings for semantic search across policy corpus
Validates quality at 95%+ accuracy target
Flags anomalies for human review
Routes clean data to claims, underwriting, and data warehouse systems

Business Outcomes:

95% extraction accuracy (vs. current manual rework at 30-40%)
60% faster claims processing (from 2-4+ weeks to days)
80% error reduction downstream
Infrastructure for Scale: Foundation for future automation opportunities

Timeline: Phase 4 (5 days) + Phase 5 (30 days) = 6 weeks to production-ready system

Use Case 2: Policy-as-Code (Decision Engine)

Status: ROADMAP — Q2 2026 (Post-UC1)

Problem: Extracted policy data sits in downstream systems; underwriting still requires manual rule application for eligibility, exclusions, and benefit determination.

Solution: Convert structured policy data from UC1 into executable business rules:

Auto-generate eligibility rules from policy conditions
Translate exclusions into decision logic
Map benefits to coverage terms
Execute rules in real-time against customer claims
Provide audit trail for compliance

Business Outcomes:

Fully automated claims decision-making (eligible/not-eligible/review)
Consistent rule application across all claims
Reduction of manual underwriting workload
Faster claim approval cycles
Improved regulatory compliance via audit trails

Prerequisites: UC1 completion, validated extraction accuracy, underwriting rule documentation

Note: This proposal focuses on UC1 (Policy Data Extraction) as the Phase 4/5 deliverable. UC2 is positioned as a natural successor application once UC1 data foundation is established and validated.

Phase Breakdown

Phase 4: Prototype (5 Days)

Deliverable: Interactive HTML/JS browser demo showing:

Document upload + extraction workflow
Quality dashboard with confidence scoring
Side-by-side document vs. extracted data
Claims adjuster + underwriter personas
Stakeholder sign-off

Phase 4 Part 2: Kyndryl Agentic Framework - Non Functional Requirements

Deliverable: Governance and operational requirements covering:

17 core capability dimensions (Agent Identity, A2A Protocol, Tool Governance, LLM Management)
Observability, Monitoring, Explainability, Human-in-Loop workflows
Token economics, Cost control, Responsible AI guardrails
Graceful degradation, Multi-tenancy, Testing & simulation
Integration with 13-step extraction pipeline

Phase 5: POC (30 Days)

Deliverable: Working extraction system with:

Real AI agents on Azure
50+ policy documents processed
Integration to claims, underwriting, data warehouse
Performance metrics dashboard
Full documentation + handover

Technology Stack

LLM: Azure Document Intelligence + Claude/GPT-4 (flexible)
Framework: Kyndryl Agent Builder (LangChain/CrewAI)
Cloud: Azure (client tech stack)
Data: 50+ real Insurance Client policy documents + synthetic variations

Resources & Budget

Kyndryl Team: 3.2 FTE (10 weeks)

AI Architect + Extraction Engineer + DevOps + QA

Insurance Client Support:

1 dedicated point person (5 days Phase 4, ongoing Phase 5)
50+ policy document samples
Weekly steering meetings
Azure environment access
Stakeholder reviews

                Estimated Investment: Commercial terms to follow based on detailed requirements workshop
            

Risks & Mitigations

Document OCR Quality: Use Azure DI + LLM validation; test diverse samples

LLM Hallucinations: Quality gates; human-in-the-loop for low confidence

Policy Domain Nuance: Underwriting/claims team input; validation rules

Privacy/Data Confidentiality: Anonymize samples; Privacy Act compliance; Data Governance approval

Next Steps

This Week: Kickoff meeting with SIVA + stakeholders

Week 1-2: Phase 4 prototype build + daily standups

Week 2: Prototype demo + stakeholder feedback

Week 3-4: Refine scope based on feedback; prepare for Phase 5

Week 5-10: POC build, testing, integration, documentation

Roadmap: UC2 (Policy-as-Code)

UC1 creates trusted structured data from documents. UC2 converts that data into executable rules (eligibility, exclusions, benefits) for consistent automated decision-making.

Timeline: UC2 follows upon UC1 completion and approval (Q2 2026 candidate).