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Everything You Need to Know About False Declines


Everything You Need to Know About False Declines

It’s never pleasant to be accused of a wrongdoing you didn’t commit. Yet, this happens to online shoppers when their legitimate purchases are falsely declined.

To e-commerce merchants, false declines are evidence that sometimes the cure is worse than the disease. Card-not-present (CNP) fraud can and does occur, costing online merchants more than $6 billion per year. But automated fraud detection tools can be overzealous, shutting down more valid transactions than fraudulent ones.

You don’t want your legitimate customers to suffer because of the actions of a nefarious few. Is it possible to stop fraud without punishing people who just want to make a purchase? It is – once you know what to look for.

In this guide, we’ll explain everything you need to know to understand what false declines are, why they happen, how much they cost you, and how to prevent them.

What Are False Declines?

Every online transaction must pass through several gateways before it’s approved, with filters at each step configured to spot the indicators of fraud.

Sometimes, one of these filters will “catch” and block an entirely legitimate transaction. This is called a false decline (or a false positive). There are two types of declines:

  • Hard declines are the result of an error or issue that cannot be resolved immediately. The decline isn’t temporary, and subsequent attempts with the same payment method will likely not be successful.
  • Soft declines are due to temporary issues and can be retried. Subsequent transaction attempts with the provided payment method information may process successfully.

False declines happen more often than you might expect. Fifteen percent of all cardholders have experienced a false decline in the past year (according to Javelin), and 58% of declined transactions are legitimate orders.

How Do False Declines Happen?

False declines are almost always triggered by automated fraud prevention software. If we follow a transaction through the payment chain, you’ll see that it’s checked for fraud multiple times. Each occasion is an opportunity for a false decline.

Step 1: The customer places an order. So far, the order has neither been approved or declined.

Step 2: The payment gateway processes the order. Depending on how they’re configured, payment gateways may run orders through a fraud filter. Typically, these filters are highly automated and unsophisticated, unable to assess “gray areas” such as unusually large purchases made for a special occasion.

Step 3: A third-party fraud protection system processes the order. If the merchant uses a fraud protection system, the system will use an additional layer of automated filters to conduct a more in-depth analysis of the order. The system may use advanced machine learning techniques to “learn” the specific fraud characteristics of a business. Nevertheless, these automated systems are not infallible. For example, they may struggle with holiday scenarios, when customers place more orders than usual to be delivered to multiple addresses.

Step 4: The issuing bank authorizes the order. Banks have their own automated processes to identify fraudulent orders. When they decline a transaction, banks will provide a response code to indicate the reason, but the code can be vague.

Step 5: Settlement of the payment. At this point, the transaction has been approved, but technical issues between the customer and the bank may still disrupt the process.

What Do Fraud Filters Look For?

Fraud filters are powered by complex algorithms that use the common characteristics of fraud as inputs. Fraud filters take things like location, delivery address, and shipping speed into account, but they go far beyond those basics. Credit card companies are reluctant to share the elements of their complex algorithms (so as not to tip their hands to fraudsters), but according to some reports, fraud-detection systems can weigh up to 500 factors.

Recent security breaches and increasing sophistication among fraudsters have made financial institutions even more assertive in their fraud prevention efforts. Banks and credit card companies have expanded their fraud criteria in the hopes of capturing more deceitful transactions – but more legitimate buyers have gotten swept up in the process.

For example, wealthy customers who shop while they travel abroad for work or pleasure may get tagged by overzealous fraud filters.

What Are the Costs of False Declines?

Imagine a false positive from the point of view of a buyer. You spend hours researching a product online, pouring through reviews, comparing features, and shopping for the best deal. Finally, you decide it’s time to pull the trigger on a big-ticket item, only to have your purchase blocked. How does that make you feel about the company you had decided was worthy of your hard-earned money and exhaustive research process? Would you stick around and try your payment again (if it was allowed) or would you try your luck with the next open tab in your browser?

The main cost of false declines comes in the form of irate customers who, more often than not, take their business elsewhere – perhaps never to return – and spread their poor experiences through their social networks.

Here are four ways false declines can hurt your business’s bottom line:

1. Less Revenue

While many e-commerce merchants fixate on the financial impact of fraud, most don’t realize that false declines can cost them up to 13 times as much. Javelin reports that U.S. merchants lose nearly $118 billion each year to falsely declined transactions.

The average online store declined 2.6% of all incoming orders because of fraud concerns, according to the Merchant Risk Council’s 2017 Global Fraud Survey. These same merchants declined 3.1% of all orders valued over $100. To recoup the financial loss of even one false decline, you might need to complete 12 or more good transactions.

2. Dissatisfied Customers

False declines aren’t simply one-off revenue hits; they tend to have a compounding effect. In e-commerce, customers are considered in terms of their lifetime value. A single loyal customer can be worth several one-and-done buyers. Losing a loyal customer doesn’t just mean losing a sale. It means losing a significant investment in marketing and customer service.

From a customer’s perspective, a false decline can be extremely disconcerting. More than 80% of cardholders who experienced a false decline said it wasn’t just inconvenient – it was embarrassing and aggravating. In Javelin’s survey of 3,200 U.S. consumers, 32% said they wouldn’t shop with a merchant again following a decline.

To make matters worse, more affluent cardholders account for over half of all false declines (as MasterCard reports). This group of customers can have a tremendously high lifetime value to e-commerce merchants.

3. A Worsened Reputation

Having a purchase declined for no apparent reason can leave a bad taste in your mouth and send you looking for an outlet to vent your frustration.

It’s a customer service truism that customers are more likely to talk about their negative experiences than their positive ones. One American Express study found that consumers tell an average of nine people about their good experiences, but they tell 16 people about the bad.

These days, poor buying experiences don’t just spread by word-of-mouth. They’re enshrined for all to see on social media, product reviews, and business review sites. This, in turn, shapes the decisions of future potential customers. Due to a cognitive bias called the “negativity effect,” consumers tend to perceive negative ratings and feedback as more credible than positive information.

How To Respond to Social Media Complaints About Your E-Commerce Business

Whether it’s because of false declines, quality issues with your product, shipping delays, or other problems, your e-commerce business will get complaints on services like Twitter and Facebook. It’s a good idea to stay on top of what people are saying about your company on social media so you can nip any dissatisfaction in the bud before it tarnishes your reputation.

Here are a few tips:

  • Be proactive. Respond early and positively to customer complaints. Don’t assume they will blow over.
  • Be empathetic. Show that you really hear and understand your customers’ concerns.
  • Be creative. You can turn negative comments into opportunities by demonstrating a commitment to making things better.
  • Be thorough. Provide a detailed explanation of the cause of the problem and how you intend to resolve it.
  • Be transparent. Resist the urge to hide or delete negative feedback; doing so may escalate the situation.
  • Be human. The personal touch can go a long way toward defusing tense situations. Let your customers know they’re dealing with real people (and you see them as real people, too).
  • Be accessible. Take the conversation offline, if necessary, into the realm of email or phone communication.
  • Be prepared to solve the problem. Problems like false declines don’t have to be a fact of doing business online. As you’ll learn below, you can take measures to reduce or eliminate false declines so future customers will have no reason to complain.

4. Decreased Fraud Detection Accuracy

Good fraud detection depends on good data. So, ironically, declining too many legitimate transactions can lead to even more false positives. For example, if you decide to decline all purchases from a certain country – because of a few bad experiences or because you simply feel the country is too much of a risk – you won’t just lose legitimate customers. You’ll miss out on valuable transaction data that can help you make better fraud-screening decisions in the future.

If you are experiencing an increase in false positives due to incomplete transaction analysis or the use of incomplete data sets, you may also see your fraud detection system’s accuracy become skewed over time. In the long run, this will amplify damage to your revenue and your brand.

How Can You Tell You Have a Problem With False Declines?

The truth is that you may never know for sure how many false declines potential customers experience through your e-commerce website. Unless customers tell you, you may remain blissfully unaware of how much money your fraud detection system is leaving on the table.

You can, however, get a sense of how often payments are falsely declined by monitoring social media and review sites like Yelp.

Another strategy is to investigate the individual details of each denied transaction to determine whether they were, in fact, fraudulent. The most reliable way to perform a fraud check is to contact cardholders directly. This process will give you insight into how many legitimate orders you’re losing to false declines, and the changes you may need to make to reduce false declines.

(If you have a high volume of transactions, it may be more practical to contact a random sample of cardholders.)

How Can You Reduce False Declines?

Giving up on fraud protection entirely would endanger your business and your customers. But it is possible to prevent fraudulent transactions without driving away customers who get caught in the middle.

Here are some ways to lower your false decline rate while keeping your business safe from fraud:

Understand Why Declines Occur

You should know why your fraud-detection system flags and blocks purchases. What criteria does it use to identify a high-risk transaction?

Many systems will prevent certain purchases automatically, such as first-time visitors making exceptionally large orders or orders that originate in certain countries. You know your customers best. Is this kind of behavior suspicious to you? If not, optimize your system to increase the success rate of future transactions.

Reject Transactions Based on Data, Not Assumptions

Wholesale generalizations (“all orders from China are frauds,” “customers will never want to ship to multiple addresses”) are rarely based in fact. Make sure you’re making decisions based on data, not instinct. If you feel you don’t understand the data, a fraud protection partner can help.

Contact Customers Directly

A questionable transaction may be an opportunity to forge a lasting relationship with a customer. Most people appreciate the chance to explain themselves rather than being rejected outright.

Before flagging a transaction, contact your customer immediately to verify the transaction details. Your customer will appreciate that you’re looking out for them.

Rely on Technology

Advances in fraud-detection technology are made every year. The latest services use artificial intelligence and machine learning to process transactions quickly, recognizing patterns or triggers that indicate potential threats.

However, artificial intelligence still has a long way to go before it can match the fraud-detection capabilities of the human mind. People are unpredictable. If a customer shops in an unexpected way, making a purchase overseas, for example, it can throw a machine algorithm for a loop.

Review Transactions Manually

Performing manual reviews will help you flag genuinely fraudulent orders, approve more legitimate purchases, learn to distinguish between the two, and improve the accuracy of future reviews. But it can also be time-consuming and labor-intensive. Nearly 30 percent of online orders would likely be subject to review.

Even in very large companies, in-house screening times are typically small. The average fraud team has only two or three people. These small teams are usually judged on the company’s chargeback rate, which incentivizes them to err on the side of caution – to the point where they’ll decline a transaction at the slightest hint of a red flag.

Compounding the issue, most in-house screening teams don’t even know what they’re missing. Less than 30 percent of companies track false positives, and 42 percent don’t know their false positive rate.

The best approach to reducing false declines is combining the best of both worlds, the efficiency of automated machine learning systems with the precision and mental flexibility of expert human teams.

How Does the Modern Manual Review Process Work?

Manual review of transactions isn’t as “manual” as it used to be. Technology and advanced techniques have made it more precise and efficient. For example, a single fraud specialist can review multiple orders at once using group analysis. Another approach has two or three analysts working in parallel; their results are compared to make a final decision.

Technology has also expanded the resources available to reviewers. Social networks, link analysis, and data visualization provide up-to-date information for reviewers to work with.

Can Manual Review Work in Real Time?

Some customers can’t wait for a manual review of their transactions. Delays on digital content, event tickets, or on-demand delivery of groceries may drive customers away.

It is possible to construct a manual review process that reduces false declines and makes decisions in real time – if you have access to the resources and expertise. The program would include one of two elements:

  • Damage control, in which your customer service team would temporarily whitelist customers who challenge a declined order. Customers would have the option of resubmitting their orders for manual review.
  • Control groups review, in which reviewers analyze random batches of automatically declined orders to identify false positives. This wouldn’t necessarily prevent all false declines, but it would help you improve your data to reduce your rate over time.

How ClearSale Combines Manual Review and Machine Learning: An Example of the Hybrid Approach

The ClearSale e-commerce fraud solution follows a four-step process that layers the expertise of two human analysts over a proven statistical algorithm. The result is the lowest false decline rates in the industry.

Step 1: A Customer Places an Order

The moment a customer lands on a website page, the ClearSale system knows where the customer came from. Website orders are sent directly to us through the ClearSale application, which integrates with most major e-commerce platforms.

Step 2: Our AI Scans the Order

Our algorithm looks for common fraud patterns, leveraging a powerful, proprietary machine-learning platform, plus a series of fraud rules adapted specifically to the merchant. The algorithm assigns a fraud score to each order.

If the score falls within a certain threshold, the order is approved. If not, it is sent for expert review. (Most other systems auto-decline these orders, the majority of which are legitimate.)

Step 3: A Fraud Analyst Manually Reviews the Order

Our expert human agents are trained to see beyond the algorithm, capturing details that machines cannot. Our fraud analysts consider all the evidence, including:

  • Do we have enough data to approve this order?
  • Is any previous fraud associated with this customer or this product?
  • What does this customer’s previous purchase behavior tell us?
  • Does the customer data match up?
  • Do we have any other insights from external data sources or social media?

If the analyst determines the order is not fraudulent, they approve the order. If not, the transaction proceeds to a second round of review.

Step 4: A Second Analyst Validates the Fraud Finding

A second analyst will take another look at the evidence, and if necessary, will contact the customer. If the second analyst determines no fraud is present, the order is immediately approved. If they are still unable to verify it, then, and only then, can the order be declined.

Are You Leaving Money on the Table?

As an e-commerce merchant, you’re driven by two things: to grow your business and satisfy your customers. An out-of-control false decline rate can jeopardize both objectives. But with the right fraud protection partner, you can cut down on false positives or eliminate them entirely.

With ClearSale in your corner, you’ll have access to the best the fraud protection world has to offer: technology built on the latest advances in artificial intelligence and machine learning, and the kind of human expertise that only comes from years of experience. Click here to learn more about ClearSale CNP Fraud Protection.

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