About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – Transaction Monitoring
- Language – English
- Category – Fraud Detection
- Prompt Title – AI Prompt for Detecting Fraudulent Transactions in Real-Time
Prompt Details
This prompt is designed for real-time fraud detection across various AI platforms. It adapts to the specific transaction data provided, maximizing its effectiveness in identifying potentially fraudulent activities.
**Prompt Structure:**
“`
Analyze the following transaction for potential fraud:
Transaction Data: {Provide a JSON object containing the transaction details}
Contextual Information: {Optional: Provide relevant contextual information}
Fraud Detection Focus: {Specify the types of fraud to prioritize}
Output Requirements: {Define the desired output format and information}
“`
**Detailed Explanation of Each Section:**
**1. Transaction Data (Required):** This section contains the core transaction information, structured as a JSON object for easy parsing by AI models. Include as many relevant fields as possible. Example:
“`json
{
“transaction_id”: “TX1234567890”,
“timestamp”: “2024-10-27T10:30:00Z”,
“amount”: 120.50,
“currency”: “USD”,
“merchant_id”: “MERCHANT001”,
“merchant_category_code”: “5812”, // Restaurant
“card_number”: “************1234”,
“card_expiry”: “12/25”,
“card_type”: “VISA”,
“customer_id”: “CUSTOMER0001”,
“ip_address”: “192.168.1.1”,
“device_id”: “DEVICEABCDEFG”,
“billing_address”: {
“street”: “123 Main St”,
“city”: “Anytown”,
“state”: “CA”,
“zip_code”: “90210”,
“country”: “US”
},
“shipping_address”: {
“street”: “456 Oak Ave”,
“city”: “Othertown”,
“state”: “NY”,
“zip_code”: “10001”,
“country”: “US”
},
“previous_transactions”: [ /* Array of past transactions for this customer, if available */ ]
}
“`
**2. Contextual Information (Optional):** This section provides additional information that can enhance fraud detection accuracy. Examples include:
* **Customer Profile:** Known customer behavior patterns, past fraud flags, account age, etc.
* **Device Fingerprinting:** Device information, browser details, OS, etc.
* **Geolocation:** GPS coordinates, proximity to known fraud hotspots, etc.
* **Velocity Checks:** Number of transactions within a specific timeframe.
* **Network Information:** Information related to the network used for the transaction.
**3. Fraud Detection Focus (Required):** This section guides the AI to prioritize specific types of fraud. Examples:
* `”Card-Not-Present Fraud”`
* `”Account Takeover”`
* `”Identity Theft”`
* `”Suspicious Money Laundering Activity”`
* `”Promo Abuse”`
* `”Friendly Fraud”` (chargeback fraud)
Focusing the AI’s analysis improves efficiency and reduces false positives.
**4. Output Requirements (Required):** Specify the desired output format and the information needed. Examples:
* `”Fraud Score (0-100)”`: A numerical score indicating the likelihood of fraud.
* `”Fraud Probability”`: The estimated probability of the transaction being fraudulent.
* `”Fraud Explanation”`: A brief explanation of the factors contributing to the fraud score/probability.
* `”Specific Fraud Type (if detected)”`: Identify the specific type of fraud suspected.
* `”Recommended Action”`: Suggest actions based on the analysis, e.g., `Approve`, `Decline`, `Review`, `Flag for investigation`.
* `”JSON format”`: Request the output in JSON format for easy integration.
**Example Prompt:**
“`
Analyze the following transaction for potential fraud:
Transaction Data: { *Provide the JSON transaction data as shown above* }
Contextual Information: {“customer_segment”: “High Value”, “past_fraud_flags”: 0, “account_age_days”: 365}
Fraud Detection Focus: “Card-Not-Present Fraud”
Output Requirements: {“Fraud Score (0-100)”, “Fraud Explanation”, “Recommended Action”, “JSON format”}
“`
**Best Practices:**
* **Clear and Concise Language:** Use precise and unambiguous language in the prompt.
* **Specific Instructions:** Provide detailed instructions on the desired output format and content.
* **Relevant Context:** Include relevant contextual information to improve accuracy.
* **Iterative Refinement:** Test and refine the prompt based on the AI’s performance.
* **Error Handling:** Consider how the prompt should handle missing or incomplete data.
This dynamic prompt framework allows you to adapt to various transaction scenarios and optimize fraud detection across different AI platforms. Remember to adjust the provided JSON structure and parameters based on your specific needs and the data available.