Identify credit cards by logo using AI

Below is a free classifier to identify credit cards by logo. Just upload your image, and our AI will predict which credit card brand it belongs to - in just seconds.

credit cards by logo identifier

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Get started

    import nyckel
    
    credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
    nyckel.invoke("credit-cards-by-logo", "your_image_url", credentials)
                

    fetch('https://www.nyckel.com/v1/functions/credit-cards-by-logo/invoke', {
        method: 'POST',
        headers: {
            'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
            'Content-Type': 'application/json',
        },
        body: JSON.stringify(
            {"data": "your_image_url"}
        )
    })
    .then(response => response.json())
    .then(data => console.log(data));
                

    curl -X POST \
        -H "Content-Type: application/json" \
        -H "Authorization: Bearer YOUR_BEARER_TOKEN" \
        -d '{"data": "your_image_url"}' \
        https://www.nyckel.com/v1/functions/credit-cards-by-logo/invoke
                

How this classifier works

To start, upload your image. Our AI tool will then predict which credit card brand it belongs to.

This pretrained image model uses a Nyckel-created dataset and has 15 labels, including Amex, Cartes Bancaires, Diners Club, Discover, Elo, Instapayment, Interac, Jcb, Maestro and Mastercard.

We'll also show a confidence score (the higher the number, the more confident the AI model is around which credit card brand it belongs to).

Whether you're just curious or building credit cards by logo detection into your application, we hope our classifier proves helpful.

Recommended Classifiers

Need to identify credit cards by logo at scale?

Get API or Zapier access to this classifier for free. It's perfect for:



  • Brand Verification: This function can be used by financial institutions to verify the authenticity of credit card logos during application processing. By ensuring that submitted logos match known brands, banks can reduce the chances of fraud and unauthorized card use.

  • Marketing Analysis: Retailers can leverage this image classification function to analyze the credit card logos used by customers during transactions. Insights gained can help tailor marketing strategies by understanding customer preferences and the prominence of specific brands.

  • Fraud Detection: E-commerce platforms can implement this function in their payment gateways to detect and flag potential fraudulent transactions. By identifying mismatched logos on submitted cards, these platforms can enhance their security measures and protect against chargebacks.

  • Payment Gateway Optimization: Payment processing companies can use this system to streamline their operations by ensuring that only accepted credit card logos are processed. This reduces errors and improves the efficiency of payment transactions, leading to higher customer satisfaction.

  • Financial Education: Educational platforms can utilize the image classification function in teaching materials to help users recognize major credit card brands. By providing visual aids, these platforms can improve financial literacy regarding credit usage and brand identification.

  • Loyalty Program Integration: Retail businesses can incorporate this identifier to ensure that customers are using branded credit cards eligible for loyalty rewards. Correct identification can automatically enroll customers in programs, enhancing the shopping experience and encouraging brand loyalty.

  • User Experience Improvement: Mobile banking apps can implement this classification feature to personalize user interfaces based on commonly used credit card logos. By displaying relevant rewards or promotions related to specific brands, apps can enhance user engagement and satisfaction.

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