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Agent Playbook

Browse agentic AI tasks that instruct LLMs to carry out specific jobs.
These can be integrated into existing workflows in your apps and tools.

Fintech

Marketplace

Insurance

Legal and Compliance

Engineering

Sales

Support

Claims Policy Verification

Claims Policy Verification

Claims Triage

Claims Triage

Claims Fraud Analysis

Claims Fraud Analysis

Customer Query Categorisation

Customer Query Categorisation

Policy Comparison

Policy Comparison

Damage Assessment from Images

Damage Assessment from Images

Insurance Needs Analysis

Insurance Needs Analysis

Policy Renewal Recommendation

Policy Renewal Recommendation

Claims Status Update

Claims Status Update

Compliance Documentation Check

Compliance Documentation Check

Claims Documentation Validation

Claims Documentation Validation

Query Triage and Escalation

Query Triage and Escalation

Claims Investigator

Claims Fraud Analysis

This agentic task detects and analyses potential insurance fraud patterns - critical for claims investigation agents. The task specification outlines how to:

  • Analyse cross-referential anomalies across claims data and supporting documentation

  • Generate risk-weighted investigation priorities with confidence metrics

  • Identify suspicious claim elements through pattern recognition algorithms

  • Produce structured fraud analysis reports with evidence-based recommendations


As a modular agentic task, it can be integrated into claims management and investigation workflows, enabling AI agents to execute systematic fraud detection within existing systems. The task specification includes outputs for anomaly detection, risk scoring, evidence assessment, and investigation prioritisation, adaptable to motor, home, and health insurance products.

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Example outputs:

"response": {
    "red_flags": [
      "Receipt for Rolex watch dated 5 days before the incident",
      "Bank withdrawal of £15,000 shortly before the purchase of the Rolex",
      "Neighbour did not hear any disturbance during the alleged break-in",
      "Incident reported to insurance before contacting the police",
      "Social media post about financial difficulties two weeks before the incident",
      "Policy taken out only 3 months ago with high-value items coverage"
    ],
    "confidence_score": "High",
    "fraud_probability": "High",
    "recommended_actions": [
      "Conduct a thorough investigation into the purchase and ownership of the Rolex watch",
      "Verify the authenticity of the police report and photos",
      "Interview neighbours and other potential witnesses",
      "Review Mr Johnson's financial records for any inconsistencies",
      "Assess the credibility of the claim given the recent policy inception and high-value items"
    ],
    "suspicious_elements": [
      "Timing of the Rolex purchase and bank withdrawal",
      "Lack of disturbance reported by neighbours",
      "Order of reporting the incident",
      "Recent social media activity indicating financial stress",
      "Short duration since policy inception"

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Claims Triage

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Customer Query Categorisation

Customer Query Categorisation