Détecteur de réservations frauduleuses : identification de transactions de voyage suspects avec l'IA

• •Compte Google et informations d'identification Gmail API
• •Point de terminaison HTTP Webhook (généré automatiquement par n8n)
• •Peut nécessiter les informations d'identification d'authentification de l'API cible
• •Informations d'identification Google Sheets API
• •Clé API Google Gemini

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  "name": "Fraudulent Booking Detector: Identify Suspicious Travel Transactions with AI",
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        "jsCode": "const bookingData = $input.first();\nconst geoData = $node['IP Geolocation Check'].json;\nconst agentResponse = $node['AI Agent'].json.output;\n\n// Parse AI agent response\nlet aiAnalysis;\ntry {\n  // Extract JSON from agent response\n  const jsonMatch = agentResponse.match(/\\{[\\s\\S]*\\}/);\n  if (jsonMatch) {\n    aiAnalysis = JSON.parse(jsonMatch[0]);\n  } else {\n    throw new Error('No JSON found in response');\n  }\n} catch (e) {\n  console.error('AI Analysis parsing error:', e);\n  // Fallback analysis\n  aiAnalysis = {\n    risk_score: 50,\n    risk_level: 'MEDIUM',\n    reasons: ['AI analysis parsing failed - manual review required'],\n    fraud_indicators: ['System error'],\n    recommendation: 'REVIEW'\n  };\n}\n\n// Calculate additional rule-based risk factors\nlet additionalRiskScore = 0;\nlet riskFactors = [];\n\n// Amount-based risk assessment\nconst amount = parseFloat(bookingData.booking_amount);\nif (amount > 10000) {\n  additionalRiskScore += 25;\n  riskFactors.push('Very high transaction amount (>$10,000)');\n} else if (amount > 5000) {\n  additionalRiskScore += 15;\n  riskFactors.push('High transaction amount (>$5,000)');\n} else if (amount < 10) {\n  additionalRiskScore += 10;\n  riskFactors.push('Unusually low amount - potential testing');\n}\n\n// Geographic risk assessment\nif (geoData.country === 'Unknown' || geoData.status === 'fail') {\n  additionalRiskScore += 20;\n  riskFactors.push('IP geolocation failed or unknown');\n} else if (['TOR', 'VPN', 'Proxy'].includes(geoData.proxy || '')) {\n  additionalRiskScore += 15;\n  riskFactors.push('VPN/Proxy detected');\n}\n\n// Time-based pattern analysis\nconst bookingTime = new Date(bookingData.booking_time);\nconst hour = bookingTime.getHours();\nif (hour < 6 || hour > 23) {\n  additionalRiskScore += 10;\n  riskFactors.push('Unusual booking time (late night/early morning)');\n}\n\n// Payment method risk evaluation\nconst highRiskPayments = ['cryptocurrency', 'prepaid_card', 'gift_card', 'mobile_payment'];\nif (highRiskPayments.includes(bookingData.payment_method?.toLowerCase())) {\n  additionalRiskScore += 15;\n  riskFactors.push(`High-risk payment method: ${bookingData.payment_method}`);\n}\n\n// Combine AI score with rule-based score\nconst aiRiskScore = aiAnalysis.risk_score || 0;\nconst combinedRiskScore = Math.min(100, Math.round((aiRiskScore + additionalRiskScore) / 2));\n\n// Determine final risk level\nlet finalRiskLevel;\nif (combinedRiskScore >= 80) finalRiskLevel = 'CRITICAL';\nelse if (combinedRiskScore >= 40) finalRiskLevel = 'HIGH';\nelse if (combinedRiskScore >= 40) finalRiskLevel = 'MEDIUM';\nelse finalRiskLevel = 'LOW';\n\n// Combine all risk factors and indicators\nconst allRiskFactors = [...riskFactors, ...(aiAnalysis.reasons || [])];\nconst allFraudIndicators = [...(aiAnalysis.fraud_indicators || []), 'Rule-based analysis completed'];\n\n// Determine final recommendation\nlet finalRecommendation;\nif (finalRiskLevel === 'CRITICAL') finalRecommendation = 'BLOCK';\nelse if (finalRiskLevel === 'HIGH') finalRecommendation = 'REVIEW';\nelse finalRecommendation = 'APPROVE';\n\nreturn {\n  user_id: bookingData.user_id,\n  booking_amount: bookingData.booking_amount,\n  risk_score: combinedRiskScore,\n  risk_level: finalRiskLevel,\n  recommendation: aiAnalysis.recommendation || finalRecommendation,\n  risk_factors: allRiskFactors,\n  fraud_indicators: allFraudIndicators,\n  ai_analysis: aiAnalysis.reasons || ['No AI analysis available'],\n  location: `${geoData.city || 'Unknown'}, ${geoData.country || 'Unknown'}`,\n  ip_address: bookingData.ip_address,\n  timestamp: new Date().toISOString(),\n  session_id: bookingData.session_id,\n  payment_method: bookingData.payment_method,\n  ai_raw_score: aiRiskScore,\n  rule_based_score: additionalRiskScore\n};"
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        "url": "https://oneclicktracker.in/booking/fraud/block-user",
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      "name": "Journaliser vers Google Sheets",
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        "operation": "append",
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          "value": "gid=0",
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      "position": [
        220,
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      "parameters": {
        "options": {},
        "respondWith": "json",
        "responseBody": "={\n  \"status\": \"processed\",\n  \"risk_level\": \"{{$node['Enhanced Risk Calculator'].json.risk_level}}\",\n  \"risk_score\": {{$node['Enhanced Risk Calculator'].json.risk_score}},\n  \"recommendation\": \"{{$node['Enhanced Risk Calculator'].json.recommendation}}\",\n  \"message\": \"Transaction analyzed: {{$node['Enhanced Risk Calculator'].json.risk_level}} risk detected\",\n  \"actions_taken\": \"{{$node['Enhanced Risk Calculator'].json.risk_level === 'CRITICAL' ? 'User blocked, critical alert sent' : $node['Enhanced Risk Calculator'].json.risk_level === 'HIGH' ? 'Flagged for review, alert sent' : 'Logged for monitoring'}}\",\n  \"fraud_indicators_count\": {{$node['Enhanced Risk Calculator'].json.fraud_indicators.length}},\n  \"session_id\": \"{{ $('Extract Booking Data').item.json.body.session_id }}\"\n}"
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      "position": [
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      "parameters": {
        "text": "=Analyze this booking transaction for potential fraud patterns and suspicious behavior:\\n\\n**Transaction Details:**\\n- User ID: {{$node['Extract Booking Data'].json['user_id']}}\\n- Amount: ${{$node['Extract Booking Data'].json['booking_amount']}}\\n- Booking Time: {{$node['Extract Booking Data'].json['booking_time']}}\\n- IP Location: {{$node['IP Geolocation Check'].json['city']}}, {{$node['IP Geolocation Check'].json['country']}}\\n- Payment Method: {{$node['Extract Booking Data'].json['payment_method']}}\\n- Session ID: {{$node['Extract Booking Data'].json['session_id']}}\\n\\n**Please provide your analysis in the following JSON format:**\\n{\\n  \\\"risk_score\\\": [number between 0-100],\\n  \\\"risk_level\\\": \\\"[LOW/MEDIUM/HIGH/CRITICAL]\\\",\\n  \\\"reasons\\\": [\\\"reason1\\\", \\\"reason2\\\", \\\"reason3\\\"],\\n  \\\"fraud_indicators\\\": [\\\"indicator1\\\", \\\"indicator2\\\"],\\n  \\\"recommendation\\\": \\\"[APPROVE/REVIEW/BLOCK]\\\"\\n}",
        "options": {
          "systemMessage": "You are an advanced fraud detection AI specialist with expertise in identifying suspicious booking patterns and fraudulent transactions. Your role is to analyze booking data comprehensively and identify potential fraud indicators.\\n\\n**Analysis Framework:**\\n1. **Amount Analysis**: Evaluate transaction amounts for unusual patterns (too high, too low, round numbers)\\n2. **Temporal Patterns**: Check for suspicious timing (late night bookings, rapid successive transactions)\\n3. **Geographic Risk**: Assess location-based risks and IP geolocation inconsistencies\\n4. **Payment Method Risk**: Evaluate payment method security levels\\n5. **Behavioral Anomalies**: Identify unusual booking patterns or session behaviors\\n\\n**Risk Scoring Guidelines:**\\n- 0-25: LOW risk - Normal transaction patterns\\n- 26-50: MEDIUM risk - Some concerning factors, monitor closely\\n- 51-75: HIGH risk - Multiple red flags, requires human review\\n- 76-100: CRITICAL risk - Severe fraud indicators, immediate action needed\\n\\n**Response Format**: Always respond with valid JSON containing risk_score, risk_level, reasons array, fraud_indicators array, and recommendation. Be specific and actionable in your analysis."
        },
        "promptType": "define"
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      "typeVersion": 1.9
    },
    {
      "id": "fe64d367-c1b7-4d4b-b45c-bafce9d52948",
      "name": "Google Gemini Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
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      ],
      "parameters": {
        "options": {},
        "modelName": "=models/gemini-2.5-pro"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "RvSkIBjP48ORJKhU",
          "name": "Google Gemini(PaLM) Api account - test"
        }
      },
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    {
      "id": "8eb4697e-38b8-4629-9169-28b34cd3fafb",
      "name": "Vue d'Ensemble du Workflow",
      "type": "n8n-nodes-base.stickyNote",
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      "parameters": {
        "width": 600,
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        "content": "## Fraud Detection Workflow Overview\n\nThis workflow is designed to detect and manage fraudulent booking transactions by integrating various checks and AI-powered analysis. It processes incoming booking data, performs IP geolocation, enriches data with AI insights, calculates risk scores, and takes automated actions based on the risk level. Finally, it logs all transactions and sends appropriate notifications."
      },
      "typeVersion": 1
    },
    {
      "id": "73367c54-cf16-4254-b9dd-0e8e2534235a",
      "name": "Notes d'Ingestion des Données",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "color": 4,
        "width": 500,
        "height": 240,
        "content": "## 1. Initial Data Ingestion & Extraction\n\n-   **Booking Transaction Webhook**: This node acts as the entry point for the workflow, receiving incoming booking transaction data via a `POST` request to the `/fraud-detection` endpoint.\n-   **Extract Booking Data**: This `Set` node extracts key booking details (user ID, amount, timestamp, IP address, payment method, location, session ID) from the incoming webhook payload, making them easily accessible for subsequent nodes."
      },
      "typeVersion": 1
    },
    {
      "id": "817d9283-4967-485e-be6a-323613db855d",
      "name": "Notes de Géolocalisation & IA",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1020,
        480
      ],
      "parameters": {
        "color": 5,
        "width": 600,
        "height": 250,
        "content": "## 2. IP Geolocation and AI Analysis\n\n-   **IP Geolocation Check**: This `HTTP Request` node enriches the booking data by fetching geolocation details (city, country) based on the extracted IP address.\n-   **AI Agent (Langchain)**: This AI Agent node uses the `Google Gemini Chat Model` to analyze the booking and geolocation data. It provides an AI-driven `risk_score`, `risk_level`, `reasons`, `fraud_indicators`, and `recommendation` in a structured JSON format.\n-   **Google Gemini Chat Model**: This node provides the large language model capabilities to the AI Agent for fraud analysis."
      },
      "typeVersion": 1
    },
    {
      "id": "a7fe1d15-9007-42b6-a2ec-f06cda926232",
      "name": "Notes de Calcul des Risques",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -880,
        -280
      ],
      "parameters": {
        "color": 6,
        "width": 600,
        "height": 260,
        "content": "## 3. Risk Calculation and Decision Logic\n\n-   **Enhanced Risk Calculator**: This `Code` node combines the AI-generated risk score with additional rule-based risk factors (e.g., amount, unusual time, payment method, IP geolocation status) to calculate a `combinedRiskScore` and `finalRiskLevel`.\n-   **Critical Risk Check**: This `If` node checks if the `risk_level` is `CRITICAL`.\n-   **High Risk Check**: This `If` node checks if the `risk_level` is `HIGH` or `MEDIUM`.\n\nBased on the risk level, the workflow branches into different paths for further action."
      },
      "typeVersion": 1
    },
    {
      "id": "d5b0ffe4-cfdd-4c4b-a2a5-c57ea75b2193",
      "name": "Notes d'Action & Notification",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        0,
        -280
      ],
      "parameters": {
        "color": 3,
        "width": 600,
        "height": 240,
        "content": "## 4. Action & Notification\n\n-   **Block User Account**: If `CRITICAL` risk, this `HTTP Request` node triggers an external system to block the user.\n-   **Gmail1**: If `CRITICAL` risk, this `Gmail` node sends a critical fraud alert email.\n-   **Flag for Review**: If `HIGH` risk, this `HTTP Request` node flags the transaction for manual review in an external system.\n-   **Gmail**: If `HIGH` risk, this `Gmail` node sends a high-risk transaction email for review."
      },
      "typeVersion": 1
    },
    {
      "id": "ee5f8df1-f361-49ff-943c-78873c58b96a",
      "name": "Notes de Journalisation & Réponse",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        40,
        340
      ],
      "parameters": {
        "color": 2,
        "width": 600,
        "height": 200,
        "content": "## 5. Logging & Response\n\n-   **Log to Google Sheets**: This `Google Sheets` node logs all transaction details, risk analysis, and actions taken to a Google Sheet for record-keeping and auditing.\n-   **Send Response**: This `Respond to Webhook` node sends a final JSON response back to the originating system, summarizing the fraud analysis status, risk level, score, recommendation, and actions taken for the session."
      },
      "typeVersion": 1
    },
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      "name": "Envoyer un message",
      "type": "n8n-nodes-base.gmail",
      "position": [
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      ],
      "webhookId": "72497b63-f6a0-4d82-9560-781bed9c28c0",
      "parameters": {
        "sendTo": "abc@gmail.com",
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        "options": {},
        "subject": "=🚨 CRITICAL FRAUD ALERT - {{ $('Extract Booking Data').item.json.body.user_id }} - ${{ $('Extract Booking Data').item.json.body.amount }}"
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        "options": {},
        "subject": "=⚠️ High Risk Transaction - Review Required - {{ $('Extract Booking Data').item.json.body.user_id }} "
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      "credentials": {
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          "id": "PcTqvGU9uCunfltE",
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