REFERENCE
Where autonomous AI agents genuinely add value in insurance — and where simpler solutions work better. Each use case maps to a workflow pattern you can design in the Agent Design Canvas exercise.
Not every AI application needs an agent. An agentic use case is one where the AI system must autonomously perform multiple steps, make decisions about what to do next, use tools to interact with external systems, and potentially loop back to correct itself — all without a human directing each step.
The task requires a sequence of actions where each step depends on the outcome of the previous one
The agent must decide what to do next based on what it observes — not just follow a fixed script
The agent calls external systems — databases, APIs, document stores — and chooses which tools based on context
For high-stakes decisions, the agent prepares a recommendation and escalates to a human for final approval
Each use case below maps to one or more of these patterns (explored in detail in the interactive explainer):
| Pattern | What It Does | Insurance Analogy |
|---|---|---|
| 🔗 Chaining | Steps execute in sequence — output of one becomes input to the next | Claims pipeline: intake → validate → assess → decide |
| ⚡ Parallelization | Multiple tasks run simultaneously, results combined | Underwriting: query medical bureau + credit bureau + occupation risk at the same time |
| 🔀 Routing | Agent classifies the input and sends it to the right specialist path | Claims triage: simple claim → auto-process; complex claim → senior adjuster |
| 🎼 Orchestration | A coordinator agent manages multiple specialist agents | End-to-end underwriting: one agent coordinates medical, financial, and occupational assessments |
Claims is where agentic AI delivers the most immediate, measurable value. Industry data supports this: 37% of insurance AI deployments in Q4 2025 were in claims management (Digital Insurance, 2026). The combination of document-heavy workflows, ambiguous inputs, and routing decisions makes it a natural fit for autonomous agents.
Pattern: Routing + Tool Use | Complexity: 🟡 Medium
Receives First Notice of Loss (FNOL) from any channel — mobile app, web portal, email, or call transcript. The agent extracts structured data (policy number, date of loss, claim type, supporting documents), validates that the policy is active and covers the reported event, checks for duplicate claims, and routes to the appropriate processing track.
| Before | Manual intake: staff reads each submission, keys data into system, checks policy status, decides routing. Average 25–40 minutes per claim. |
| After | Agent handles intake in under 2 minutes. Simple claims (estimated 40–60% of volume) go straight-through. Staff focus on complex cases only. |
| Key metric | Intake-to-assignment time reduced from hours to minutes; staff redeployed to high-value adjudication work |
Pattern: Parallelization + Orchestration + Human-in-the-Loop | Complexity: 🔴 High
A multi-agent system processes hospitalization and outpatient claims. One agent extracts diagnosis codes and treatment details from medical reports. Another validates against policy benefits (panel hospital, co-pay, deductibles, sub-limits). A third checks for pre-existing condition exclusions. An orchestrator consolidates results and either auto-approves within authority limits or escalates with a decision-ready summary.
| Before | Claims assessor manually reviews medical report, checks policy schedule, calculates payable amount. 30–90 minutes per claim depending on complexity. |
| After | Simple hospitalization claims (within panel, clear diagnosis, within limits) auto-adjudicated in minutes. Assessors review only the agent's recommendation for complex cases. |
| Key metric | Claims resolution time from 5–10 business days to 1–2 days for straightforward cases |
Pattern: Chaining (detect → investigate → package) + Tool Use | Complexity: 🔴 High
Monitors incoming claims for fraud signals — velocity patterns (multiple claims in a short period), geographic anomalies (claim location vs. policyholder address), document inconsistencies, and network analysis (linked claimants or providers). When triggered, it assembles an investigation package: timeline of events, related claims, provider history, and a risk score with explainable reasoning.
| Before | Fraud detected reactively through manual review or tip-offs. Investigation takes days to compile evidence from multiple systems. |
| After | Proactive detection at point of claim submission. Investigation package assembled in minutes, ready for human investigator review. |
| Key metric | Fraud detection rate improvement; investigation preparation time from days to hours; reduced claims leakage |
Underwriting requires gathering information from multiple external sources, reasoning about complex risk interactions, and making judgment calls about what additional information to request. This open-ended, multi-source reasoning is what distinguishes it from simple rule engines.
Pattern: Parallelization + Orchestration + Human-in-the-Loop | Complexity: 🔴 High
Processes new life insurance applications through a multi-agent pipeline. One agent extracts applicant data and medical history from application forms. Another queries external data sources (medical information bureau, credit bureau) in parallel. A third applies underwriting rules — BMI, smoking status, family history, occupation class — and generates a risk classification (Preferred / Standard / Substandard / Decline). The orchestrator produces a decision recommendation with supporting rationale.
| Before | Underwriter manually reviews application, requests external reports, waits for responses, applies rules. Average 3–5 days for standard cases, weeks for complex ones. |
| After | Standard risk cases (healthy, young, low sum assured) processed in hours with underwriter confirmation. Complex cases get a pre-assembled decision package. |
| Key metric | Underwriting turnaround from days to hours for standard cases; underwriter capacity focused on genuinely complex risks |
Policy servicing has one use case that genuinely benefits from agentic AI — where the agent must monitor, reason about timing, personalize interventions, and manage a multi-step reinstatement process with conditional logic.
Pattern: Chaining (monitor → alert → intervene → reinstate) | Complexity: 🟡 Medium
Monitors policies approaching lapse (premium grace period expiring), generates personalized outreach (payment reminders, alternative payment options, reduced paid-up options), and when a lapse occurs, guides the reinstatement process — checking eligibility, required evidence of insurability, and outstanding premium calculations.
| Before | Batch reminder letters sent on fixed schedule. Lapsed policies require manual reinstatement processing. Persistency rates: industry average 85–88% (13-month). |
| After | Proactive, personalized intervention before lapse. Automated reinstatement for eligible cases. Target: 2–4% persistency improvement. |
| Key metric | Persistency rate improvement (each 1% = significant revenue retention); reduced reinstatement processing time |
Compliance work involves iterative investigation, cross-referencing multiple regulatory sources, and assembling evidence packages. The two use cases below require genuine reasoning — not just data extraction.
Pattern: Parallelization + Chaining + Tool Use | Complexity: 🔴 High
Compiles regulatory submissions (MAS Forms, RBC calculations, actuarial returns) by extracting data from multiple source systems in parallel, performing cross-system reconciliation, applying validation checks, flagging data quality issues, and generating submission-ready reports with anomaly commentary.
| Before | Compliance team manually extracts data from multiple systems, reconciles, applies formulas in spreadsheets. Quarterly reporting takes 2–4 weeks of intensive effort. |
| After | Agent assembles draft submission with data from all sources, pre-validated and reconciled. Compliance team reviews and approves rather than building from scratch. |
| Key metric | Reporting preparation time reduced by 50–70%; fewer data quality errors; more time for analysis vs. data gathering |
Pattern: Chaining (screen → investigate → draft) + Tool Use | Complexity: 🟡 Medium
Performs enhanced due diligence on flagged transactions — screening against sanctions lists, PEP (Politically Exposed Persons) databases, and adverse media. When suspicious activity is confirmed, drafts a Suspicious Activity Report (SAR) with structured narrative, supporting evidence, and regulatory-compliant formatting for the compliance officer to review and submit to STRO.
| Before | Compliance analyst manually screens, investigates, and drafts SAR. Each case: 4–8 hours. Backlog during peak periods. |
| After | Agent performs screening and assembles investigation package. Draft SAR prepared for compliance officer review. Case preparation time reduced to 1–2 hours. |
| Key metric | SAR preparation time reduced by 60–75%; faster filing within regulatory timelines; reduced backlog |
One distribution use case genuinely requires agentic AI — where the system must conduct a multi-turn conversation, dynamically adapt its questions, calculate gaps, and match products.
Pattern: Multi-turn conversation + Tool Use + Chaining | Complexity: 🟡 Medium
Conducts a structured needs analysis conversation — gathering life stage, dependents, existing coverage, income, liabilities, and financial goals. Calculates protection gaps (death, disability, critical illness, hospitalization), recommends appropriate coverage amounts, and generates a compliant advisory report for the financial advisor to review with the client.
| Before | Advisor manually conducts needs analysis (30–60 min), calculates gaps in spreadsheet, prepares report. Total: 2–3 hours per client including documentation. |
| After | Agent pre-gathers information and calculates gaps. Advisor reviews with client, adjusts recommendations, and finalizes. Preparation time reduced to 15–30 minutes. |
| Key metric | Advisor productivity (more clients served per day); consistent quality of needs analysis; better compliance documentation |
One additional use case is emerging but not yet widely deployed:
Pattern: Chaining + Tool Use | Complexity: 🟡 Medium
When MAS issues a new circular, consultation paper, or regulatory update, the agent reads the document, identifies which AnyCompany policies/processes/systems are affected, assesses the scope of impact (minor wording change vs. major operational overhaul), and generates an impact assessment report with recommended actions and timelines.
| Before | Compliance team manually reads regulatory updates, assesses impact through meetings with business units. Initial assessment: 1–3 weeks. |
| After | Agent produces draft impact assessment within hours of regulatory publication. Compliance team validates and refines rather than starting from scratch. |
| Key metric | Time-to-initial-assessment reduced from weeks to days; no regulatory changes missed; faster compliance response |
Being honest about what doesn't need agentic AI is just as important as identifying what does. These are common insurance tasks that are better served by simpler solutions:
| Task | Why NOT Agentic | Better Solution |
|---|---|---|
| Policy change processing (address, payment mode, beneficiary) | Fixed rules, predictable paths, no reasoning needed | Workflow engine / BPM with validation rules |
| Motor damage photo assessment | Vision model + cost lookup is a fixed pipeline, not autonomous reasoning | ML pipeline (vision model → parts DB → cost estimate) |
| Commission calculation | Deterministic math with fixed rules — no judgment required | Rule engine / finance system with validation |
| Group insurance quoting | Census → pricing model → proposal is a fixed pipeline | Actuarial pricing tool + document generation |
| Customer FAQ chatbot | RAG + tool calls is well-solved — it's conversational AI, not agentic AI | RAG-powered chatbot with policy data access |
| PDPA data subject requests | Query systems + compile + redact is a fixed workflow | Data catalog + automated query + redaction rules |
| IFRS 17 cohort calculations | Deterministic actuarial math — must be exact, auditable, reproducible | Actuarial modeling platform (e.g., Prophet, AXIS) |
| Advisor meeting prep | Pull data from CRM + summarize is GenAI, not agentic | GenAI assistant with CRM integration |
Use this framework to evaluate whether a workflow in your team is a genuine candidate for agentic AI:
| Complexity | Characteristics | Examples |
|---|---|---|
| 🟡 Medium | Multi-step chains, external integrations, conditional routing, personalization | Claims triage (#1), Lapse prevention (#5), SAR drafting (#7), Needs analysis (#8), Reg change impact (#9) |
| 🔴 High | Multi-agent orchestration, parallel processing, open-ended investigation, complex reasoning | Auto-adjudication (#2), Fraud investigation (#3), Automated underwriting (#4), Regulatory reporting (#6) |
These use cases connect directly to what you'll learn and practice in the workshop:
| Workshop Module | What You Learn | How These Use Cases Apply |
|---|---|---|
| M1: From LLMs to Agents | The agent loop: Observe → Plan → Act → Reflect | Understand why fraud investigation needs an agent (open-ended reasoning) vs. commission calculation (fixed rules) |
| M2: Exploring Agentic AI | Agent types, memory, tool use, autonomy levels | See how agents interact with insurance systems (policy admin, claims DB, external bureaus) |
| M3: Agentic Workflows | Chaining, Parallelization, Routing, Orchestration | Map each use case to its pattern — this is what you'll practice in the Design Canvas |
| M6: Custom Solutions | Guardrails, human-in-the-loop, evaluation | Every use case above has guardrails — these are the production patterns you'll design |
Pick a use case from this page (or bring your own workflow) → choose a pattern → fill out the Agent Design Canvas
| # | Domain | Use Case | Pattern | Complexity |
|---|---|---|---|---|
| 1 | Claims | Claims Intake & Triage | Routing + Tool Use | 🟡 |
| 2 | Claims | Medical Claims Auto-Adjudication | Parallelization + Orchestration | 🔴 |
| 3 | Claims | Fraud Detection & Investigation | Chaining + Tool Use | 🔴 |
| 4 | Underwriting | Automated Life Insurance Underwriting | Parallelization + Orchestration | 🔴 |
| 5 | Policy | Lapse Prevention & Reinstatement | Chaining | 🟡 |
| 6 | Compliance | MAS Regulatory Reporting | Parallelization + Chaining | 🔴 |
| 7 | Compliance | AML/CFT Screening & SAR Drafting | Chaining + Tool Use | 🟡 |
| 8 | Distribution | Financial Needs Analysis | Multi-turn + Tool Use | 🟡 |
| 9 | Compliance | Regulatory Change Impact Assessment | Chaining + Tool Use | 🟡 (emerging) |