False declines explained: Why they happen and how businesses can prevent them

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Meer informatie 
  1. Inleiding
  2. What are false declines?
  3. Why do false declines happen?
  4. How to prevent false declines
  5. How Stripe prevents false declines

As online transaction volume grows over time, there is an increasing number of legitimate purchases that are wrongfully rejected. In addition to the financial blow from lost revenue, these “false declines” can erode customer trust and dampen brand loyalty, as frustrated customers are more likely to abandon their shopping carts and turn to competitors.

Businesses need to balance implementing fraud prevention tactics and providing quick, intuitive, convenient shopping experiences for customers. But first, businesses need to understand the reasons behind false declines before implementing strategies to reduce them.

Below, we’ll review the important aspects of false declines: what they are, why they happen, what causes them, and best practices to prevent them—without undermining a simple and secure transaction experience for customers. Here’s what you need to know.

What’s in this article?

  • What are false declines?
  • Why do false declines happen?
  • How to prevent false declines
  • How Stripe prevents false declines

What are false declines?

False declines, also known as “false positives,” occur when a legitimate transaction is rejected or declined by a bank or payment processor.

Why do false declines happen?

False declines often happen due to the systems and algorithms that detect and prevent fraudulent activity. While these systems are designed to protect both customers and financial institutions, they can sometimes be overcautious or contain inaccuracies that lead to the rejection of valid transactions.

Understanding the reasons for false declines can help customers and businesses take preventative measures and manage such occurrences more effectively. Here are some common reasons why false declines happen:

  • Unusual spending patterns
    It is standard practice for banks and credit card companies to monitor the spending patterns of their customers. If a transaction is not consistent with historical spending behavior, such as an unusually large purchase or a series of rapid transactions, these monitoring systems may flag it as potentially fraudulent. While this measure can prevent fraud, it sometimes catches legitimate transactions.

  • Technical errors
    Transaction processing involves complex systems and networks. Sometimes, a technical glitch, network lag, or communication error between different systems can result in a transaction being declined. This can occur even if there is nothing inherently suspicious or unusual about the transaction.

  • Exceeding limits
    Financial institutions often set daily or transactional limits on accounts to mitigate the potential losses from fraud. If a customer attempts to make a purchase that exceeds these predefined limits, the transaction might be declined. While such limits can be a useful fraud prevention measure, they can also cause frustration for a customer who wants to make a large—but legitimate—purchase.

  • Incorrect information
    During a transaction, the customer’s information—such as their billing address, card security code, and expiration date—needs to be verified. If there is any discrepancy between this information and what the bank has on file, the transaction may be declined. This is a common security measure to ensure that the person making the transaction is the legitimate cardholder.

  • Outdated authorization techniques
    Some payment processors might use outdated fraud detection algorithms. As fraud methods evolve, so must the algorithms designed to detect them. Outdated systems might not be able to discern between legitimate and fraudulent activity, resulting in more false declines.

  • Strict fraud detection algorithms
    Some fraud detection algorithms are overly cautious, flagging legitimate transactions with a small resemblance to potentially fraudulent patterns.

  • Geographical triggers
    Transactions made from geographic locations that are far from a customer’s home or region, or from regions that are commonly associated with fraudulent activities, are often viewed as suspicious. For example, if a US-based customer who rarely or never travels abroad suddenly makes a purchase in a different country, this might trigger a security flag.

  • Expired cards or accounts
    Using an expired credit card or a closed account can also cause the transaction to be declined. The customer might not be aware that their card has expired or that there has been a change in their account status, leading to a decline when they attempt to make a legitimate transaction.

Customers can take steps to prevent false declines, including notifying their bank before making large or international purchases. Businesses can work with payment processors that use more sophisticated and adaptive fraud detection algorithms to reduce the occurrence of false declines. Below is more information about how businesses can engineer a highly sensitive fraud detection system that’s minimally disruptive to legitimate transactions.

How to prevent false declines

To reduce the occurrence of false declines, businesses need to balance fraud prevention with a smooth customer experience. Here are some key strategies and tactics businesses can use to tackle false declines:

  • Use advanced fraud detection tools
    Enable modern fraud detection systems that use machine learning and artificial intelligence. These systems can analyze vast amounts of data in real time, which makes them adept at distinguishing between genuine and fraudulent transactions without causing unnecessary false declines.

  • Customize fraud detection settings
    Many payment processors allow businesses to customize the settings of their fraud detection tools. By adjusting these settings to be more lenient or more reflective of the business’s customer base and transaction patterns, businesses can reduce false declines.

  • Implement multifactor authentication (MFA)
    Instead of declining a transaction outright, businesses can require additional verification for transactions that are flagged as suspicious. For example, sending a one-time password to the customer’s registered mobile number or email address can add a layer of security without declining the transaction right away.

  • Regularly update customer data
    Keep customer data, such as addresses and phone numbers, up-to-date. This will ensure that the information used for verification during transactions is current, reducing the chance of declines due to mismatches in information.

  • Educate customers on account limits
    Inform customers of any daily spending limits or restrictions on their accounts, and educate them about how they can change these limits, if needed. This can prevent genuine transactions from being declined due to exceeding preset limits.

  • Allowlisting trusted customers
    If a business has repeat customers who have a history of legitimate transactions, these customers can be allowlisted. This means that transactions from these customers undergo less stringent checks, reducing the likelihood of false declines.

  • Analyze data
    By analyzing transaction data, businesses can identify patterns and trends that can help them understand which transactions are likely to be false declines. This information can then be used to fine-tune fraud detection systems.

  • Seek customer feedback
    Encourage customers to provide feedback if they experience a false decline. This feedback can be invaluable for understanding the reasons behind false declines and making necessary adjustments to fraud detection systems.

  • Monitor and review declined transactions
    Regularly review transactions that have been declined to identify any patterns or commonalities among false declines. This can help in adjusting fraud detection measures appropriately.

For all of these tactics, choosing the right payment processing provider is key. How your payment processor handles false declines can either make the process effortless or frustrating.

How Stripe prevents false declines

Stripe’s sophisticated approach to fraud detection and prevention reduces false declines through a combination of machine learning and data analysis. Here’s an overview of how Stripe engineers payment systems that are as resistant as possible to false declines:

  • Large datasets
    Stripe processes billions of dollars in transactions every year, creating a vast dataset that machine learning algorithms can use to identify patterns and trends more effectively. Stripe’s algorithms analyze this data to better understand what constitutes a typical transaction versus fraud.

  • Real-time learning and adaptation
    Stripe’s machine learning models are capable of real-time learning. As they process transactions, they continuously analyze the outcomes and adapt accordingly. This real-time adaptation allows the models to stay up-to-date with the latest fraud patterns without compromising the approval rates of legitimate transactions.

  • Customization for different businesses
    Stripe understands that businesses are diverse and what might be considered a normal transaction for one might be unusual for another. Therefore, Stripe’s machine learning models adapt to the specific transaction patterns of individual businesses. This customization reduces the likelihood that a legitimate transaction will be falsely flagged as fraudulent due to industry-specific norms.

  • Risk scoring
    Stripe assigns each transaction a risk score, which represents the probability that the transaction is fraudulent. Businesses can use these risk scores to set their own thresholds for when a transaction should be automatically declined or flagged for manual review. This gives businesses more control and flexibility in managing fraud prevention.

  • Stripe Radar
    Stripe Radar uses machine learning to evaluate transactions for fraud. Radar considers hundreds of signals about each transaction and uses data from the entire Stripe network to detect and prevent fraud, while automatically adapting to the changing patterns of fraud. Businesses can also set custom rules to fit their own requirements.

  • Authentication tools
    Stripe supports 3D Secure, an authentication tool that adds an additional layer of verification for card payments. Even if a transaction is flagged as potentially risky, the customer can still complete the purchase by providing additional verification, reducing the chance of a false decline.

By incorporating these elements into its measures for fraud detection and prevention, Stripe reduces false declines while maintaining a strong defense against fraudulent transactions. To learn more and get started with Stripe, go here.

The content in this article is for general information and education purposes only and should not be construed as legal or tax advice. Stripe does not warrant or guarantee the accurateness, completeness, adequacy, or currency of the information in the article. You should seek the advice of a competent attorney or accountant licensed to practice in your jurisdiction for advice on your particular situation.

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