Capture UPI Payment Instructions with an AI Agent

This guide explains how to capture UPI payment instructions using an AI agent, enabling you to automate the extraction of payment details from natural language conversations, messages, or documents. You’ll learn how to set up the agent, define the data schema, and handle common edge cases. By the end, you’ll have a reliable system that converts free-text instructions into structured, actionable payment requests.

What You’ll Need

• An AI agent platform (e.g., a custom GPT, a chatbot framework like Rasa, or a cloud AI service) that supports structured data extraction.
• Access to the UPI payment system’s API or a payment gateway that accepts UPI mandates (for the final execution step, if you plan to automate the actual payment).
• A list of valid UPI IDs (e.g., name@bank) for validation purposes.
• Basic knowledge of JSON or similar structured data formats for defining the output schema.

Step-by-Step

1. Define the data schema. Determine the exact fields you need to extract from a UPI payment instruction. At minimum, these are: UPI ID (payee), amount (in INR), and a reference/note. Optionally, you may want to capture the payer’s UPI ID, the date, or a recurring flag. Create a JSON schema that specifies the field names, types, and any constraints (e.g., amount must be a positive number).
2. Train or configure the AI agent. If you’re using a pre-built LLM (like GPT-4), provide it with a clear system prompt that explains the task: “Extract the UPI payment details from the user’s message. Output a JSON object with fields: upi_id, amount, note. If any field is missing, set it to null.” For a custom model, you’ll need to collect a dataset of example instructions and train it accordingly.
3. Implement the extraction logic. In your agent’s code, after receiving a user message, call the AI model with the message and your schema. Parse the model’s response into a structured object. Ensure you handle cases where the model returns extra fields or misformats the data.
4. Validate the extracted data. Before proceeding, validate the extracted UPI ID format (it should look like ‘name@bank’), the amount (must be a positive number, and within any limits you set), and the note (optional, but should be a string). If validation fails, ask the user to rephrase or confirm the details.
5. Handle missing information. If the agent extracts only partial information (e.g., the amount but not the UPI ID), prompt the user for the missing details. For example: “I found the amount is ₹500. Please provide the UPI ID to send it to.”
6. Confirm with the user. Once all fields are extracted and validated, present a summary to the user for confirmation. For instance: “You want to pay ₹500 to john@okhdfc with note ‘Lunch’. Proceed?” This step is crucial to avoid errors.
7. Execute the payment (optional). If you’re automating the payment, call the UPI API with the confirmed details. Ensure you handle the response for success or failure and relay that to the user.
8. Log and learn. Keep a log of extraction failures and successes. Periodically review the logs to refine your prompts or retrain the model to improve accuracy.

Common Mistakes

• Assuming the AI always gets the UPI ID right. UPI IDs are case-insensitive but must be in the correct format. Always validate against a regex pattern like ^[\w.-]+@[a-zA-Z]+$.
• Not handling multiple amounts. Some instructions might mention amounts in different contexts (e.g., “pay ₹500 for the bill, but I also owe ₹200 for taxi”). Ensure your extraction logic picks the correct amount or asks for clarification.
• Ignoring currency symbols. The AI might extract ‘500’ without the ‘₹’ symbol. Always assume the currency is INR unless specified otherwise, but flag if a different currency appears.
• Overcomplicating the schema. Keep the schema as simple as possible. Adding too many fields increases the chance of extraction errors.
• Skipping user confirmation. Automatically executing a payment without confirmation can lead to costly mistakes. Always confirm before sending money.

Tips

• Use a few-shot prompting technique: include a couple of examples in your system prompt to guide the model’s output format.
• If using an LLM, set the temperature to 0 to get more deterministic outputs.
• Consider using a two-step process: first extract the intent (is this a payment request?), then extract the details. This reduces false positives.
• For recurring payments, add a ‘frequency’ field to the schema and ask the user if they want to set up a mandate.
• Always sanitize the extracted data before using it in API calls to prevent injection attacks.

Troubleshooting

Issue: The AI extracts the wrong UPI ID. Check if the user’s message contains multiple UPI IDs. If so, ask the user to specify which one. Also, verify that the AI is not hallucinating an ID that looks plausible but isn’t in the message.

Issue: The amount is missing or incorrect. The user might have written the amount in words (e.g., “five hundred”). In that case, you’ll need a number-to-words converter. If the amount is ambiguous, ask the user to confirm.

Issue: The AI outputs invalid JSON. This can happen with LLMs. Implement a JSON parser that can recover from common errors, or use a schema-constrained decoding method if your platform supports it.

Issue: The user’s message is in a language other than English. If your AI agent supports multilingual input, ensure your prompt is also in that language, or instruct the model to output the extracted fields in English (e.g., the note can be in the original language).

Issue: The payment fails after extraction. This could be due to an invalid UPI ID or insufficient funds. The agent should catch the API error and relay a friendly message to the user, suggesting they check the details.

Frequently Asked Questions

Can the AI agent handle voice instructions?

Yes, if you integrate a speech-to-text service, the agent can transcribe the voice instruction and then extract the payment details from the text.

How do I ensure the AI doesn’t extract a UPI ID from a previous message in the chat?

Provide only the relevant message to the model, or use a conversation history but instruct the model to focus on the latest user message.

What if the user says ‘pay the same as last time’?

You’ll need to store previous payment details in memory or a database. The agent can then retrieve the last payment and use it as the default, but always confirm with the user.

Is it possible to capture instructions from an image, like a QR code?

If the image contains a QR code, you can use a QR scanner to decode the UPI ID and amount. For handwritten instructions, you’d need OCR and then NLP to extract the fields.

How accurate is the extraction?

Accuracy depends on the model and the clarity of the instruction. With a well-tuned prompt and validation, you can achieve high accuracy (above 95%) for straightforward instructions. For complex ones, fall back to asking the user for confirmation.

Can this work for international payments?

UPI is primarily for India. For international payments, you’d need a different payment system, but the extraction logic can be adapted to capture the necessary fields (like SWIFT/BIC).

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