False Declines: A Brief Understanding

It’s always disappointing when you are wrongly blamed for doing something that you didn’t do. However, for online shoppers, it not uncommon for their purchases to be inexplicably declined by ecommerce websites for no apparent reason.  

For e-commerce retailers, online frauds such as Card-not-present (CNP) can cost more than $6 billion per year. Often rudimentary fraud prevention systems designed eliminate CNP fraud, unintentionally increase the rate in which they block shoppers purchases who have legitimate transactions.

It is never the intention of these online businesses to hurt their loyal customers and block the bad actors. But mistakes do occur and legitimate shoppers are negatively affected.

In this article, we will help you understand all the things you’ll need to know about false declines, their causes, their cost implications and how you can prevent them.

A brief introduction of false declines

Transactions that are done online go through a very sensitive procedure, passing through many automated checks and delicate filters before they are approved. All of these lengths are taken to not only prevent fraud but to flag even the slightest hint of them. 

However, sometimes these “overactive” filters will detect something they deem to be suspicious and block a completely lawful purchase. This is termed as a false decline.

These declines are usually of two types:

  • Soft declines may occur as a result of temporary errors and can be resolved when you retry the transaction. Attempting transactions once again may prove a success for the same payment method. 
  • Hard declines are the product of an error or issue that has occurred and cannot be settled right away. This decline is permanent for a certain amount of time and successive attempts at transactions will lead to failure.

Incidence of False Declines

According to a study, false declines are usually set off by software that has been setup to prevent fraud. Transactions through the payment chain will check for fraud multiple times, looking for fractures due to a false decline.

Step 1: An order is placed by the buyer: Here an order is placed to be approved or declined.

Step 2: The order is processed by the payment gateway: Here, the payment gateway runs the orders through its fraud filter software. Usually, these fraud filters are fully automated and are incapable of assessing ambiguous orders such as abnormally hefty purchases made for specific events.

Step 3: The order is processed by third-party fraud protection system: As the order is to be approved, the e-businessmen will have automated filters to perform an even more in-depth analysis of the order. This system is highly advanced and may use advanced machine learning strategies to understand certain fraud features of a business. However, these automated systems are not foolproof as they may struggle when customers place unusually more orders during holidays and events. 

Step 4: The authorization of order by the bank: The banks have their own automated software to identify orders that may be frauds. When these banks reject a transaction

Step 5: The payment settlement: By this stage, the transaction has been accepted by the e-merchant, but issues between the customer and the bank may still have the power to block the purchase.

What Are Fraud Filters Actually Looking For?

Complex algorithms command the fraud filters use the normal characteristics of fraud as inputs. These filters look for attributes such as the location of buyer, the address of delivery and the shipping speed. The companies behind fraud prevention softwares do not share the makeup of their complex algorithms to prevent giving any ideas to fraudsters, but some studies suggest that they can use a combination of up to 500 unique factors.

Fraudsters use sophisticated methods to commit crimes every day. So financial institutions try to stay one step ahead to prevent fraud. Banks and the credit card companies have strengthened their fraud standards with an aim to detect fraud transactions but even legitimate online shoppers have faced issues in the process.

The Cost of False Decline

When you decide to order a product online, you spend hours looking for the best item. You research, check the reviews and then look for the best deals offered by different companies selling the same product. Finally you are committed to making the purchase and click ‘buy’ only to have your transaction declined for no apparent reason.

False declines are perhaps the single most frustrating experience faced by your customers, who are just one click away from going to a competitors website, never to see them again. Not only that, these bad experiences are often shared via their social media, hindering not only your existing customer base, but potential buyers as well.

There are four major ways in which false declines affect your businesses:

1. Reduced Revenue

Usually, e-commerce businesses evaluate their success when they reduce the impact that fraud cases have on their businesses. However, they do not know the real weight that false declines hold – and they can cost these e-merchants 13 times more than the actual fraud itself!

 According to the The Merchant Risk Council’s 2017 Global Fraud Survey, online stores faced an estimated 2.6% reduction in total orders due to false declined. According to the survey, approximately 3.1% of the orders valuing over $100 were also falsely declined.

2. Displeased Customers

False declines do not just affect the revenue; they have rather startling effects. In e-commerce business, a strong clientele is a lifetime achievement. Loyal customers can be worth more than several one time buyers. Likewise losing one loyal buyer does not mean that you have lost one revenue point, one time. It means that you have a customer and potentially their business for life.

3. A Bad Reputation

A declined purchase for no real reason can leave you confused and feeling frustrated. It is a common thing that people usually talk about the bad experiences more than the good ones. And this is what happens when a purchase is declined.  

Due to the ever expanding influence of social media people have taken it as the medium of transfer of views. These buyers put up bad reviews not only on the company website but also on their own social media. They spread the word of the bad experiences that they had with the certain e-commerce company and try to change the minds of the loyal buyers as well leading to a bad reputation.

4. Reduced Accuracy of Fraud Detection 

Efficient fraud detection can only happen if you have legitimate data. Likewise, the decline of legitimate transactions leads to the accumulation of false data.

For example, if one determines to reduce transactions from a specific company because of some sour experiences, your company not only loses a rightful customer but also loses precious transaction data that could have come in handy for improving future fraud-screening decisions.

Also, if your company is facing an amplified false positive rate due to incomplete transaction data sets, your fraud detection system may face inaccuracy and its data may become skewed over time, damaging your capital and the brand name.

Identifying If You Have a Problem with False Decline

No matter what software you use, your e-commerce company may never detect if your valuable customers are facing a false decline. In fact you may remain ignorant of the fact that your company is facing this issue until someone tells you so. 

Often e-commerce companies are advised to monitor social media and review sites like Yelp to know about false declines. 

You can also look into the details of each declined transaction to decide if they were, fraudulent or not. One of the most secure ways to check for fraud is to get in touch with the cardholders directly. This procedure will give you an idea into how many legitimate orders you are giving away to false declines and the improvements you need to make to decrease false declines.

How to Reduce False Declines?

Your business and buyers may be put in peril if you let go of fraud protection completely. But it is entirely possible to prevent fraud transactions while keeping your customers happy. 

Some of the ways to lower your company’s false decline rate while keeping your e-business secure from fraud, are as follows:

Leverage cutting edge technology

Fraud-detection technologies are modified every year. The most recent services use AI and machine learning to proceed with the transactions quickly, detecting patterns that could mean potential threats.

However, artificial intelligence can still never be enough for the sophisticated way that fraud is being done. People use unpredictable ways that can even confuse artificial intelligence.

Comprehend the reasons behind the incidence of fraud

You should understand the reasons behind the malfunctioning of your fraud-detection system and why it restricts purchases.

Usually, systems avert such purchases on their own, such as exceptionally large orders put by the first time buyers or orders that come from a certain countries. You recognize your clientele in the best way. Ask yourself if this kind of behavior is suspicious to you or not? If no, then modify your systems to accelerate the success rate of future transactions.

Reject Transactions after getting evidence from data

Wholesale assumptions are rarely based on facts. Always be certain that you’re making decisions based on data, not instinct. If you cannot reach a decision for understanding the data, a fraud protection solution like Spotrisk can always help.

Get in touch with Customers Directly

A suspicious transaction may be a chance to build an ever-lasting relationship with a new customer. Most people like to explain themselves instead of outright rejection. Contact your customers directly and talk to them, sharing a moment of make or break.

Review Transactions Manually

By performing manual reviews, you can flag fraudulent orders and endorse more legitimate transactions, learn the difference between the two, and better the correctness of future reviews. Advanced fraud detection systems such as Spotrisk can help with automating this process as much as possible, but manual reviews will help make the automated detection smarter in the future.

The best strategy to reduce false declines is mixing the competence of automated learning systems with the accuracy and mental suppleness of expert human beings.

Working of a Modern Manual Review Process 

Manual review of transactions does not really mean hard earned labor. Technologies and many advanced techniques have helped to make it more accurate. 

Technology has also enhanced the resources provided to the reviewers. Social networking, link analysis, and data visualization give an up-to-date information for reviewers to work with.

Can Manual Review Work in Real Time?

Some customers desperately wait for a manual review of their purchases. Any delays on the digital data, event tickets, or the delivery of groceries may make the customers go away.

With that said, it is entirely likely to build a manual review process that limits fake declines and comes to a conclusion in real-time. The program includes either one of two essentials:

  • Damage control, whereby your customer service team would for the time being whitelist those buyers who insist on a declined order. Customers would have the choice of putting up again their orders for a manual review.
  • Control groups review, whereby reviewers examine chance batches of automatically rejected orders to examine false positives. This would not necessarily avert all false declines, but it could also help you progress your data to limit your rate over time.

Are You Leaving Money on the Table?

Being an e-commerce business, you focus on two key things: the growth of your business and the satisfaction of your customers. A false decline rate can endanger both of these. But with the use of  the right fraud protection system, you can reduce false positives or end them completely.

With the help of Spotrisk, you will direct to the most excellent fraud protection the world has to offer; the technological innovations built on the modernization of artificial intelligence and machine learning, and the sort of human knowledge that is only achieved after years of industry experience.

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