Stay ahead of threats with machine learning fraud detection

Both systems utilize rules and policies to inform fraud decisioning. The main difference is that a machine learning system doesn't require any human intervention whereas a strictly rules based system does.

For a machine learning system, rules and policies are initially set, then the technology operates accordingly. Rules can be updated, but it isn’t necessary because machines constantly learn from interactions, collect data, and improve processes on their own. Humans can intervene, and likely should when a sudden threat, economic event, or natural disaster happens.

For example, if a hurricane is predicted to hit a town on the east coast, hundreds of citizens might start booking up flights and hotels in nearby towns to escape the natural disaster. Let's say you operate one of these hotels. Under typical circumstances, this activity might look suspicious, and your fraud prevention software might block some of these transactions.

But, if you manually update rules and policies just for that event, you'll avoid declining all these legitimate transactions. And making those updates are as simple as pressing a few buttons. There's no manual coding or research required.

On the other hand, in a rule based fraud detection system, humans have to continually monitor fraud trends and update rules. For example, if a new threat emerges, you would first need to catch it, then modify the system to account for new indicators of fraud. And if a major event happened, like a hurricane, you would have to create brand-new rules that account for surges in transactions.

Yes, our fraud detection software uses rules. But it also uses multiple types of machine learning and data. Combined, these three elements are what make our solution efficient, accurate, and powerful.

Almost all fraud solutions on the market today use some combination of rules and machine learning. Together, they make up a complete solution. A system that only uses one element is subpar and might make a lot of errors.

While machines can be intelligent and learn on their own, rules help them towards the right path. They need some guidelines to follow in order to achieve the results you want.

Let’s think of it another way.

Could you drive your car from your office place to your house without traffic laws, driving laws, road markings, and signs? Probably. But you might make some errors along the way and face a variety of obstacles that slow you down. You’ll get to your destination faster and safer if there are rules and guidelines that you can follow.

Want to know more about fraud detection rules? Check out our blog.

Machine learning can pick up on trends and patterns that the human brain simply can’t catch during manual reviews. Not only that, but machines learn constantly from data, picking up subtle indicators of fraud that influence decisioning. As a result, you get far more accurate results with a machine learning based fraud detection system. In fact, on average Kount helps businesses reduce false positives by 70% while still blocking the greatest amount of fraud.

No. Switching to a machine learning system from a manual process will not take control away from you. Machine learning is meant to replace long, error-prone manual processes. But you still get control over how the system makes decisions. That’s where rules and policies come into play.

For example, machine learning can look for fraud indicators and block or accept interactions. But it needs a set of rules or guidelines to reference in order to make those decisions. And that’s where you shine. You set the policies, you create the rules, you control the outcomes.

Plus, you can still set aside certain interactions and orders for manual review. Essentially, you get to decide how involved you are with the whole fraud decisioning process.

At the end of the day, you’re the expert on what works for your business. Our software is meant to supplement and improve upon your existing processes, not take over them.

Yes. You can set up policies that flag certain transactions for manual review. The best part is that this kind of system can also speed up the manual review process by providing a baseline of information — such as risk scores, historical data, consumer information, and more — that you don’t have to look up yourself.

Kount customers that prefer to review some orders see a significant reduction in the amount of time it takes their teams to complete manual reviews. For example, for one review, it might take an employee a half hour to an hour to complete. But with Kount, that same review may take around 30 seconds or less.

The short answer is yes, machine learning in fraud prevention systems can help reduce your chargeback rate. But there are different kinds of chargebacks, and therefore different ways of solving for each.

Machine learning engines monitor overall fraud activity and are trained to detect suspicious interactions. So, they can help stop chargebacks related to fraudulent activity, or unauthorized transactions.

However, not all chargebacks are fraud-related. Chargebacks can happen because a customer forgot about a purchase and disputed the charges with their bank. This kind of chargeback is tied to friendly fraud. And machine learning alone can’t determine when or if a customer is making a false claim. 

What machine learning can do is collect data about each transaction — data that you can then use as evidence to respond to false fraud claims and potentially win the dispute.

Nope, not at all! We believe everyone should have easy access to the best fraud protection available. So, we do all the hard work and coding for you! You don’t need to be a math expert, data scientist, or engineer. Anyone on your team can use and update our software with ease and confidence. 

We provide access to the system through a user dashboard. The dashboard is where you can monitor activity and update policies as needed. And updating policies is as simple as clicking a few buttons. No complex coding required!

Credit card fraud is an easily detectable and preventable fraud threat when you use a machine learning-based fraud detection system. And that's because machine learning systems continually collect and analyze transaction data — such as card numbers, billing and shipping addresses, location, device information, and more. As a result, the software can quickly recognize unusual credit card transactions. 

Here’s how it works.

When a card transaction is attempted, the system immediately goes to work evaluating billions of data points to determine whether the interaction is legitimate or fraudulent. 

First, the software collects data about the transaction. Then, the system connects those data points to patterns of behavior associated with the cardholder to predict whether a fraudster or the cardholder is making the purchase. Finally, the system consults business policies and accepted risk thresholds to determine whether to block or accept the transaction.