
My First AI Agent

Fraud Detection Engine
We built a fraud detection model using **n8n**, a no-code automation platform, to streamline the detection of suspicious transactions. The workflow integrates AI-powered analysis, real-time monitoring, and external tools like vector databases to identify anomalies efficiently. This solution highlights how businesses can leverage automation and AI to enhance security without requiring extensive coding expertise.
WorkFlow
Step 1: Understanding the Problem​
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We built a fraud detection model using n8n, a no-code automation platform, to streamline the detection of suspicious transactions. The workflow integrates AI-powered analysis, real-time monitoring, and external tools like vector databases to identify anomalies efficiently. This solution positively impacted key metrics such as Fraud Detection Rate, Call Quality Score, Call Handling Time, Employee Productivity, Customer Satisfaction (CSAT), Cost Reduction, and Compliance Adherence, showcasing the power of automation in enhancing security and operational efficiency.
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Step 2 Designing the Workflow:
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A Step-by-Step Workflow
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Designing an efficient fraud detection system in n8n involves integrating multiple tools and services to process data, detect anomalies, and trigger alerts in real time. Below is a structured breakdown of the workflow:
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1. Workflow Trigger: Real-Time Data Ingestion
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The workflow starts with a Webhook Node, acting as an entry point to capture incoming data—such as voice recordings or transaction details—from a CRM or other connected systems. This enables real-time processing as soon as new data is received.
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2. Data Preprocessing: Structuring Unstructured Data
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Speech-to-Text Conversion:
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If the input includes voice recordings, they are sent to a speech-to-text API (e.g., Google Speech-to-Text) via an HTTP Request Node for transcription.
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This transforms unstructured audio into structured text for further analysis.
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Data Refinement:
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An Edit Fields Node cleans and formats the transcribed text, ensuring consistency and compatibility with downstream processing.
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3. Business Logic and Anomaly Detection
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Custom Business Rules:
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A Code Node applies predefined business rules to detect anomalies—such as unusual transaction amounts or suspicious patterns in call transcripts.
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AI-Powered Detection:
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The refined data is sent to an AI service (e.g., OpenRouter API) via an HTTP Request Node, leveraging pattern recognition and vector similarity matching to identify fraudulent activities.
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4. Decision Making: Automated Fraud Evaluation
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An If Node assesses the AI’s response:
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If fraud is detected, the workflow triggers alerts and further actions.
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If no anomaly is found, the workflow terminates without unnecessary escalation.
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5. Alerting and Notifications: Immediate Action
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Upon detecting fraud, the workflow triggers:
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A Slack Node to send real-time alerts to relevant teams.
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A Google Sheets Node (or another logging mechanism) to store details for auditing.
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Additional workflows for automated follow-ups or escalations.
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6. Scalability and Future-Proofing
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The workflow is modular, allowing easy modifications or expansions.
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Seamless integration with databases, APIs, and automation tools ensures adaptability to evolving fraud patterns and business needs.
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Step 3 : Implementation
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1. Webhook Node: Real-Time Data Capture
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Connects to your CRM or relevant system to fetch voice recordings as soon as they are available, enabling real-time processing.
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2. Speech-to-Text Processing
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Code Node (Code1) + HTTP Request Node (HTTP Request1):
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Processes the voice recordings and sends them to a speech-to-text API (e.g., Google Speech-to-Text) for transcription.
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Converts unstructured audio into structured text, making it suitable for analysis.
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3. Data Refinement
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Edit Fields Node (Edit Fields1):
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Cleans, structures, and standardizes the transcribed text.
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Ensures consistency for downstream processing and AI evaluation.
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4. AI-Powered Fraud Detection
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HTTP Request Node (HTTP Request2) + Code Node (Code2):
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Sends the refined transcript data to an AI-powered service (e.g., OpenRouter API) for anomaly detection.
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Uses business rules, prompt engineering, or machine learning models to identify fraud patterns.
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5. Decision Logic
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If Node (If1):
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Evaluates AI’s response to determine if an anomaly is detected:
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If fraud is detected: The workflow triggers alerts and logs actions.
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If no anomaly is found: The workflow terminates, avoiding unnecessary alerts.
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6. Alerting & Action Logging
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Slack Node (Slack1) + Code Node (Code3):
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Slack1: Sends real-time alerts to relevant teams for immediate action.
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Code3: Logs anomalies, updates records, or triggers follow-up workflows as needed.
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Further Steps
Exhaustive testing, debugging, configuration checks
Final Output
