We all know a fraud prevention system is primordial these days. But what happens when it backfires, and why?
Fraud protection is always a compromise between security and user experience. Push your security to the max, and you risk losing sales. Make it too lax, and you open the door to fraudsters.
But how do you strike the right balance between the two? And is there anything you can do to ensure your fraud management system gives the best results? As you’ll see, it all starts with understanding the problem of false positives.
False Positives: A Definition
False positives occur when user activity is flagged as risky or fraudulent, but is actually legitimate. It can be a login, a registration, or payment attempt. The latter is the most common: 40% of American online shoppers have their credit cards transactions marked as fraudulent by over-zealous system.
Declined Sales = Revenue Loss
The ultimate goal for most online businesses is to process transactions. Any declined sale hurts the company’s bottom line. Javelin calculated that US merchants can lose as much as $118B every year to false positives,
The number is particularly worrying for the following reason: it is 13 times that of the cost of credit card fraud, which means a badly calibrated fraud prevention system can hurt your business more than not having one at all.
Frustrated Customers and Reputation Damage
In the age of social media, online reviews and transparent practices, company management has every reason to ensure customer experience is above par. And it’s easy to see how damaging it can be to frustrate customers, especially with declined card payments.
Their frustration, however, should be acknowledged. They probably spent hours researching the right product, and considered multiple options before clicking the Buy button. Being allowed to onboard but not complete a transaction adds insult to injury, as a certain amount of personal information has already been given by that point — why couldn’t the extra security measures kick in before?
Moreover, an unhappy customer isn’t just hurting sales and looking for improved services with your competitor. Their dissatisfaction often snowballs and has larger repercussions on your business, as American Express report shows that consumers tell an average of 11 people about their good experiences and 15 people about the bad.
Paired with unsatisfactory customer support, it’s easy to see why these customers take to social media and do their best to damage a business whose service they now see in a negative light — all of which started because the fraud prevention system and team made a wrong call.
False Positives Impact Detection Accuracy
Last but not least, a program that includes with too many false positives also creates a negative cycle of detection rates.
Here is an example of how it could happen:
The problem is that these 5 false positive cases are never marked as such by the system. Which means your anti fraud detection is essentially calculating risk with flawed data analytics. This, in turn, decreases the accuracy of detection overtime.
The Solutions: Data Enrichment, Error Matrixes, Machine Learning
Luckily for fraud managers, there are a number of tools available from providers to measure, identify, and reduce false positives.
The first one, data enrichment, also happens to be the easiest to use. For an in-depth guide on the topic, including how it works with compliance, please see this post.
But the key takeaway is that monitoring fewer customer data points can increase the risk of false positives. Data enrichment takes a small number of data points, finds them in external databases, and aggregates it all to give you a better idea of who you are dealing with.
For instance, working with an email address only, SEON Intelligence could tell you:
- The user’s real name
- If they registered on a free email domain
- How long they’ve been using the address
- If it’s linked to any known social media accounts
Combining data enrichment for email addresses, phone numbers, device and IP address can create a very complete view of the user, which can help separate fraudsters from real users with increased precision.
Error / Confusion Matrixes
An error, or confusion matrix is a tool used to measure the success rate of your statistical analysis. It can make rules pertaining to specific data like monetary value or time between clicks, and see how well these rules fared historically.
It is a very powerful visualization tool that helps understand the accuracy of your rules by looking at historical data, and tweak them or create new rules to reduce the false positives.
The biggest challenge of fraud detection is that the rules of the game keep changing. This is due to four key factors:
- Fraud has a long tail distribution: there are too many unique cases to create specific rules for each
- The patterns change quickly: for instance, if you are an ecommerce, fraud will change seasonally, and depending on your current inventory
- Fraudsters mimic good users: the constant game of cat and mouse means your software needs to update fast, and in real time
- Fraudsters are adversarial: they actively look for weak spots in your line of defense to exploit your business
What should be immediately clear is therefore that your rules cannot be static, stale, and rigid. In short, you need AI to help with rules-creation.
But don’t be fooled into thinking implementing a machine learning system will immediately solve all your fraud detection problems. The key is to feed all the data to the software, but to let fraud managers oversee the manual reviews by giving the right tools, such as:
- Whitebox machine learning system
- Human-readable rules
These are all features we integrate into SEON’s Sense platform, designed to give you the full power of a machine learning engine, and the most intuitive dashboard to control it.
Don’t Let a Bad Fraud Management System Hurt Your Business
Fraud protection tools come in many shapes and forms these days, which is why you have to be more vigilant than ever before choosing yours. Does it let you enrich data? Are the rules too rigid to really help? And will it create too many false positives that will eventually damage your business?
Answering these questions is just the beginning. You’ll then need to look at scaling options, pricing models, and advanced features. But hopefully, this guide on false positives will help you get a better idea of how to find the best technology and performance for your needs.