Automate Actuarial Premium Adjustments and Claims Reporting with Gpt-4.1,…

The workflow JSON

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{
  "id": "swJUjkp6o1ZZsixc",
  "name": "Real-time actuarial premium adjustment and reporting system",
  "tags": [],
  "nodes": [
    {
      "id": "31ea87f4-b6c1-4f1d-a97f-9a6a05cd3e8c",
      "name": "Schedule Trigger - Ingest Claims Data",
      "type": "n8n-nodes-base.scheduleTrigger",
      "position": [
        -2288,
        144
      ],
      "parameters": {
        "rule": {
          "interval": [
            {
              "field": "minutes",
              "minutesInterval": 15
            }
          ]
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "457618a9-6a6c-4898-bff2-9d0a2ca7ae08",
      "name": "Workflow Configuration",
      "type": "n8n-nodes-base.set",
      "position": [
        -2064,
        144
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "id-1",
              "name": "claimsApiUrl",
              "type": "string",
              "value": "<__PLACEHOLDER_VALUE__Claims API endpoint URL__>"
            },
            {
              "id": "id-2",
              "name": "premiumThreshold",
              "type": "number",
              "value": 0.05
            },
            {
              "id": "id-3",
              "name": "productTeamEmail",
              "type": "string",
              "value": "<__PLACEHOLDER_VALUE__Product team email address__>"
            },
            {
              "id": "id-4",
              "name": "riskTeamSlackChannel",
              "type": "string",
              "value": "<__PLACEHOLDER_VALUE__Risk team Slack channel ID__>"
            }
          ]
        },
        "includeOtherFields": true
      },
      "typeVersion": 3.4
    },
    {
      "id": "bff19af2-dafb-4d11-8a31-eefcc39f1b2f",
      "name": "Fetch New Claims Data",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -1840,
        144
      ],
      "parameters": {
        "url": "={{ $('Workflow Configuration').first().json.claimsApiUrl }}",
        "options": {},
        "sendHeaders": true,
        "headerParameters": {
          "parameters": [
            {
              "name": "Content-Type",
              "value": "application/json"
            }
          ]
        }
      },
      "typeVersion": 4.3
    },
    {
      "id": "f6bac2d1-19c0-4c80-8f1f-a87f1998405f",
      "name": "Actuarial Analysis Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -1568,
        64
      ],
      "parameters": {
        "text": "=Analyze the following claims data and recalculate actuarial assumptions: {{ JSON.stringify($json) }}",
        "options": {
          "systemMessage": "You are an expert actuarial analyst specializing in insurance claims analysis.\n\nYour task is to:\n1. Analyze the provided claims data for patterns and trends\n2. Recalculate key actuarial assumptions including:\n   - Loss ratios\n   - Claim frequency rates\n   - Average claim severity\n   - Reserve adequacy\n3. Use the Calculator tool for all mathematical computations\n4. Return your analysis in the structured JSON format defined by the output parser\n\nBe thorough and precise in your calculations."
        },
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 3.1
    },
    {
      "id": "4a55692e-f632-4bbb-9b26-3b6557a99598",
      "name": "OpenAI Model - Analysis",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        -1616,
        288
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4.1-mini"
        },
        "options": {},
        "builtInTools": {}
      },
      "credentials": {
        "openAiApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "08ae8720-3859-4500-a0ef-269b86418d26",
      "name": "Structured Output - Actuarial Assumptions",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        -1360,
        288
      ],
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n  \"type\": \"object\",\n  \"properties\": {\n    \"lossRatio\": {\"type\": \"number\"},\n    \"claimFrequency\": {\"type\": \"number\"},\n    \"averageClaimSeverity\": {\"type\": \"number\"},\n    \"reserveAdequacy\": {\"type\": \"number\"},\n    \"trendAnalysis\": {\"type\": \"string\"},\n    \"recommendedActions\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n  },\n  \"required\": [\"lossRatio\", \"claimFrequency\", \"averageClaimSeverity\", \"reserveAdequacy\"]\n}"
      },
      "typeVersion": 1.3
    },
    {
      "id": "1284c6e7-ea8d-4896-9aa5-f51dcb98eeee",
      "name": "Calculator Tool",
      "type": "@n8n/n8n-nodes-langchain.toolCalculator",
      "position": [
        -1488,
        288
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "19bc2520-cc93-4023-8cbd-e25e86068e54",
      "name": "Calculate Premium Adjustments",
      "type": "n8n-nodes-base.code",
      "position": [
        -1248,
        64
      ],
      "parameters": {
        "jsCode": "// Extract actuarial assumptions from AI agent output\nconst assumptions = $input.first().json;\nconst threshold = $('Workflow Configuration').first().json.premiumThreshold;\n\n// Calculate premium adjustment percentage based on loss ratio\nlet premiumAdjustment = 0;\nif (assumptions.lossRatio > 0.75) {\n  premiumAdjustment = (assumptions.lossRatio - 0.75) * 0.5; // Increase premiums\n} else if (assumptions.lossRatio < 0.60) {\n  premiumAdjustment = (assumptions.lossRatio - 0.60) * 0.3; // Decrease premiums\n}\n\n// Apply threshold\nif (Math.abs(premiumAdjustment) < threshold) {\n  premiumAdjustment = 0;\n}\n\n// Calculate new premium rates\nconst adjustmentData = {\n  ...assumptions,\n  premiumAdjustmentPercentage: Math.round(premiumAdjustment * 10000) / 100,\n  adjustmentReason: premiumAdjustment > 0 ? 'Loss ratio exceeds target' : premiumAdjustment < 0 ? 'Loss ratio below target' : 'No adjustment needed',\n  effectiveDate: new Date().toISOString().split('T')[0],\n  timestamp: new Date().toISOString()\n};\n\nreturn adjustmentData;"
      },
      "typeVersion": 2
    },
    {
      "id": "d63b5a44-47af-414a-9f21-bc4f5eb43c69",
      "name": "Memo Writer Agent Tool",
      "type": "@n8n/n8n-nodes-langchain.agentTool",
      "position": [
        -800,
        288
      ],
      "parameters": {
        "text": "=Generate an actuarial memo for: {{ $fromAI(\"context\") }}",
        "options": {
          "systemMessage": "You are a professional actuarial memo writer.\n\nYour task is to:\n1. Create a comprehensive actuarial memo documenting the premium adjustment analysis\n2. Include executive summary, methodology, findings, and recommendations\n3. Use professional actuarial terminology and formatting\n4. Return the memo in structured JSON format with sections\n\nEnsure the memo is clear, concise, and suitable for executive review."
        },
        "hasOutputParser": true,
        "toolDescription": "Generates professional actuarial memos documenting premium adjustments and analysis"
      },
      "typeVersion": 3
    },
    {
      "id": "9a4429d2-7981-437a-a329-d5c8097b64d7",
      "name": "OpenAI Model - Memo Writer",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        -784,
        496
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4.1-mini"
        },
        "options": {},
        "builtInTools": {}
      },
      "credentials": {
        "openAiApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "8fe04b91-8879-4a2b-9c6e-7283b2d2e909",
      "name": "Structured Output - Memo",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        -640,
        496
      ],
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n  \"type\": \"object\",\n  \"properties\": {\n    \"title\": {\"type\": \"string\"},\n    \"executiveSummary\": {\"type\": \"string\"},\n    \"methodology\": {\"type\": \"string\"},\n    \"findings\": {\"type\": \"string\"},\n    \"recommendations\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n    \"conclusion\": {\"type\": \"string\"}\n  },\n  \"required\": [\"title\", \"executiveSummary\", \"findings\", \"recommendations\"]\n}"
      },
      "typeVersion": 1.3
    },
    {
      "id": "c102786f-d929-4b1a-9ccd-af52db550439",
      "name": "Risk Assessment Agent Tool",
      "type": "@n8n/n8n-nodes-langchain.agentTool",
      "position": [
        -480,
        272
      ],
      "parameters": {
        "text": "=Assess risk implications for: {{ $fromAI(\"adjustmentData\") }}",
        "options": {
          "systemMessage": "You are a risk management specialist for insurance products.\n\nYour task is to:\n1. Evaluate the risk implications of the proposed premium adjustments\n2. Assess potential impacts on:\n   - Portfolio profitability\n   - Market competitiveness\n   - Regulatory compliance\n   - Customer retention\n3. Provide risk mitigation strategies\n4. Return your assessment in structured JSON format\n\nBe thorough in identifying both upside and downside risks."
        },
        "hasOutputParser": true,
        "toolDescription": "Evaluates risk implications of premium adjustments and provides risk management recommendations"
      },
      "typeVersion": 3
    },
    {
      "id": "eb2e9600-6774-4818-a5b0-30b3f8fc13d3",
      "name": "OpenAI Model - Risk Assessment",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        -448,
        496
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4.1-mini"
        },
        "options": {},
        "builtInTools": {}
      },
      "credentials": {
        "openAiApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "4ef84d90-1890-483e-85c3-f83a9abfd481",
      "name": "Structured Output - Risk Assessment",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        -256,
        496
      ],
      "parameters": {},
      "typeVersion": 1.3
    },
    {
      "id": "53841ba2-f5b0-4bab-b382-8481d08ea9e1",
      "name": "Orchestrator Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -816,
        64
      ],
      "parameters": {
        "text": "=Coordinate the generation of actuarial memo and risk assessment for the following premium adjustment data: {{ JSON.stringify($json) }}",
        "options": {
          "systemMessage": "You are an orchestrator coordinating actuarial reporting and risk assessment tasks.\n\nYour task is to:\n1. Call the Memo Writer tool to generate a comprehensive actuarial memo\n2. Call the Risk Assessor tool to evaluate risk implications\n3. Combine both outputs into a final report\n4. Return the complete report in structured JSON format\n\nEnsure both tools receive the necessary context from the premium adjustment data."
        },
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 3.1
    },
    {
      "id": "f40060b0-77f9-4ec4-9cb7-d05ad6198ecc",
      "name": "OpenAI Model - Orchestrator",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        -928,
        288
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4.1-mini"
        },
        "options": {},
        "builtInTools": {}
      },
      "credentials": {
        "openAiApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "acf2e6a4-5e93-403f-9974-e4356b10649b",
      "name": "Structured Output - Final Report",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        -176,
        320
      ],
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n  \"type\": \"object\",\n  \"properties\": {\n    \"reportDate\": {\"type\": \"string\"},\n    \"premiumAdjustment\": {\"type\": \"number\"},\n    \"actuarialMemo\": {\"type\": \"object\"},\n    \"riskAssessment\": {\"type\": \"object\"},\n    \"actionItems\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n    \"approvalRequired\": {\"type\": \"boolean\"}\n  },\n  \"required\": [\"reportDate\", \"premiumAdjustment\", \"actuarialMemo\", \"riskAssessment\"]\n}"
      },
      "typeVersion": 1.3
    },
    {
      "id": "82c82ab4-3171-4d36-a90d-f7e2edb5235d",
      "name": "Send Email to Product Team",
      "type": "n8n-nodes-base.gmail",
      "position": [
        -16,
        16
      ],
      "parameters": {
        "sendTo": "={{ $('Workflow Configuration').first().json.productTeamEmail }}",
        "message": "=<h2>Premium Adjustment Report</h2>\n<p><strong>Report Date:</strong> {{ $json.reportDate }}</p>\n<p><strong>Premium Adjustment:</strong> {{ $json.premiumAdjustment }}%</p>\n\n<h3>Actuarial Memo</h3>\n<p><strong>Title:</strong> {{ $json.actuarialMemo.title }}</p>\n<p><strong>Executive Summary:</strong> {{ $json.actuarialMemo.executiveSummary }}</p>\n<p><strong>Findings:</strong> {{ $json.actuarialMemo.findings }}</p>\n\n<h3>Risk Assessment</h3>\n<p><strong>Overall Risk Level:</strong> {{ $json.riskAssessment.overallRiskLevel }}</p>\n<p><strong>Profitability Impact:</strong> {{ $json.riskAssessment.profitabilityImpact }}</p>\n\n<h3>Action Items</h3>\n<ul>\n{{ $json.actionItems.map(item => `<li>${item}</li>`).join(\"\") }}\n</ul>\n\n<p>Please review and provide approval if required.</p>",
        "options": {},
        "subject": "=Actuarial Update: Premium Adjustment Report - {{ $json.reportDate }}"
      },
      "credentials": {
        "gmailOAuth2": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "5c2b2594-ebbb-4c3d-90b3-a9722cdb25b5",
      "name": "Send Slack Message to Risk Team",
      "type": "n8n-nodes-base.slack",
      "position": [
        -16,
        208
      ],
      "parameters": {
        "text": "=:chart_with_upwards_trend: *Premium Adjustment Alert*\n\n*Report Date:* {{ $json.reportDate }}\n*Premium Adjustment:* {{ $json.premiumAdjustment }}%\n*Risk Level:* {{ $json.riskAssessment.overallRiskLevel }}\n\n*Key Findings:*\n{{ $json.actuarialMemo.findings }}\n\n*Risk Impact:*\n\u2022 Profitability: {{ $json.riskAssessment.profitabilityImpact }}\n\u2022 Competitiveness: {{ $json.riskAssessment.competitivenessImpact }}\n\n*Action Required:* {{ $json.approvalRequired ? \"Yes - Review and approve\" : \"No - Informational only\" }}\n\nFull report sent to product team.",
        "select": "channel",
        "channelId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $('Workflow Configuration').first().json.riskTeamSlackChannel }}"
        },
        "otherOptions": {},
        "authentication": "oAuth2"
      },
      "credentials": {
        "slackOAuth2Api": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 2.4
    },
    {
      "id": "661f1089-54c7-478f-b1ca-1e3eed8357c9",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -992,
        -384
      ],
      "parameters": {
        "color": 5,
        "width": 592,
        "height": 288,
        "content": "## Prerequisites\nNVIDIA API access, OpenAI API key, claims management system API\n## Use Cases\nAuto insurance claim triage, property damage assessment automation\n## Customization\nAdjust risk scoring thresholds, add industry-specific analysis criteria\n## Benefits\nReduces claim processing time by 85%, ensures consistent evaluation standards"
      },
      "typeVersion": 1
    },
    {
      "id": "4315ad26-018e-4d4f-813c-e00bb3c529e4",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1440,
        -368
      ],
      "parameters": {
        "width": 400,
        "height": 272,
        "content": "## Setup Steps\n1. Configure claims database API credentials in \"Fetch New Claims Data\" node\n2. Input NVIDIA API key for all OpenAI Model nodes  \n3. Add OpenAI API key in Orchestrator Agent configuration\n4. Set up Calculator Tool parameters for premium adjustment calculations\n5. Configure Gmail credentials and recipient addresses for product team  \n6. Connect Slack workspace and specify risk team channel for alerts\n "
      },
      "typeVersion": 1
    },
    {
      "id": "e76a2c58-6edd-467e-b58f-834297f61d53",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2288,
        -368
      ],
      "parameters": {
        "width": 816,
        "height": 272,
        "content": "## How It Works\nThis workflow automates insurance claims processing by deploying specialized AI agents to analyze actuarial data, draft claim memos, and perform risk assessments. Designed for insurance adjusters, underwriters, and claims managers handling high claim volumes, it solves the bottleneck of manual claim review that delays settlements and increases operational costs. The system ingests new claims data via scheduled triggers, then routes information to an actuarial analysis agent that calculates loss ratios and risk scores. A memo writer agent generates detailed claim summaries with recommendations, while a risk assessment agent evaluates fraud indicators and coverage implications. An orchestrator agent coordinates these specialists, ensuring consistent analysis standards. Final reports are automatically distributed via email to product teams and Slack notifications to risk management, creating transparent workflows while reducing claim processing time from days to hours with standardized, comprehensive evaluations."
      },
      "typeVersion": 1
    },
    {
      "id": "fce724f8-d95c-40d9-a75a-8e18b68c8c98",
      "name": "Sticky Note3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1664,
        -64
      ],
      "parameters": {
        "color": 7,
        "width": 560,
        "height": 672,
        "content": "## Actuarial Analysis Execution\n**Why:** Statistical modeling identifies patterns and calculates risk metrics that inform pricing accuracy and reserve adequacy beyond human capacity at scale."
      },
      "typeVersion": 1
    },
    {
      "id": "2c15ea8b-bc44-4032-8b1c-b886c663cbdd",
      "name": "Sticky Note4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2352,
        -64
      ],
      "parameters": {
        "color": 7,
        "width": 672,
        "height": 672,
        "content": "## Scheduled Claims Data Ingestion\n**Why:** Automated retrieval ensures timely processing of new claims without manual data entry, maintaining consistent workflow cadence and preventing backlogs."
      },
      "typeVersion": 1
    },
    {
      "id": "c8b06752-a643-4d40-9eea-5ccb466c4eb0",
      "name": "Sticky Note5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1056,
        -64
      ],
      "parameters": {
        "color": 7,
        "width": 544,
        "height": 816,
        "content": "## Claim Memo Generation\n**Why:** Standardized documentation ensures regulatory compliance and consistent decision-making while eliminating time-consuming manual report writing."
      },
      "typeVersion": 1
    },
    {
      "id": "84c47650-9195-4b28-a7c9-2ba9ab1e165b",
      "name": "Sticky Note6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -496,
        -64
      ],
      "parameters": {
        "color": 7,
        "width": 672,
        "height": 992,
        "content": "## Multi-Dimensional Risk Assessment & Email\n**Why:** Parallel evaluation of fraud indicators, policy compliance, and financial exposure provides comprehensive risk profiles for informed adjudication."
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "settings": {
    "availableInMCP": false,
    "executionOrder": "v1"
  },
  "versionId": "ecdc91c2-d8ae-412a-933e-5d716d0af4ed",
  "connections": {
    "Calculator Tool": {
      "ai_tool": [
        [
          {
            "node": "Actuarial Analysis Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Orchestrator Agent": {
      "main": [
        [
          {
            "node": "Send Email to Product Team",
            "type": "main",
            "index": 0
          },
          {
            "node": "Send Slack Message to Risk Team",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Fetch New Claims Data": {
      "main": [
        [
          {
            "node": "Actuarial Analysis Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Memo Writer Agent Tool": {
      "ai_tool": [
        [
          {
            "node": "Orchestrator Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Workflow Configuration": {
      "main": [
        [
          {
            "node": "Fetch New Claims Data",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI Model - Analysis": {
      "ai_languageModel": [
        [
          {
            "node": "Actuarial Analysis Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Actuarial Analysis Agent": {
      "main": [
        [
          {
            "node": "Calculate Premium Adjustments",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Structured Output - Memo": {
      "ai_outputParser": [
        [
          {
            "node": "Memo Writer Agent Tool",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI Model - Memo Writer": {
      "ai_languageModel": [
        [
          {
            "node": "Memo Writer Agent Tool",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Risk Assessment Agent Tool": {
      "ai_tool": [
        [
          {
            "node": "Orchestrator Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI Model - Orchestrator": {
      "ai_languageModel": [
        [
          {
            "node": "Orchestrator Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Calculate Premium Adjustments": {
      "main": [
        [
          {
            "node": "Orchestrator Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI Model - Risk Assessment": {
      "ai_languageModel": [
        [
          {
            "node": "Risk Assessment Agent Tool",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Structured Output - Final Report": {
      "ai_outputParser": [
        [
          {
            "node": "Orchestrator Agent",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Structured Output - Risk Assessment": {
      "ai_outputParser": [
        [
          {
            "node": "Risk Assessment Agent Tool",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "Schedule Trigger - Ingest Claims Data": {
      "main": [
        [
          {
            "node": "Workflow Configuration",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Structured Output - Actuarial Assumptions": {
      "ai_outputParser": [
        [
          {
            "node": "Actuarial Analysis Agent",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    }
  }
}

Credentials you'll need

Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.

• gmailOAuth2
• openAiApi
• slackOAuth2Api

About this workflow

This workflow automates insurance claims processing by deploying specialized AI agents to analyze actuarial data, draft claim memos, and perform risk assessments. Designed for insurance adjusters, underwriters, and claims managers handling high claim volumes, it solves the…

Source: https://n8n.io/workflows/12494/ — original creator credit. Request a take-down →

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