Machine Learning is a buzzword in fraud prevention, but is it always the best tool for businesses?
Looking at Forbes’ list of the Top 10 Machine Learning Use Cases, you’ll find industries that won’t surprise you.
Self-driving cars, search recommendations and financial trading have long known to rely on machine learning efficiently. But you’ll also notice 3 of the items on it have to do with security. Namely: data security, personal security, and fraud detection.
It seems machine learning technology is particularly effective in these fields. The algorithms know how to look for patterns in data, extract them and apply rules that refine themselves over time.
So does that mean you can just go ahead and invest in ML (machine learning) to solve all your fraud detection needs? Not necessarily.
Where Machine Learning Works Best Against Fraud
With Machine Learning, your first immediate benefit is an ability to process enormous amounts of data. No human could trawl through as many transactions, login details and IP addresses as a computer. And if the amount scales over the years, machines can still process them without batting a (digital) eyelid.
This means a talent for recognising patterns that humans might not initially spot. In technical terms, we are talking about confusion matrix analysis, and it can work with any data, from currency to age groups. For instance, an anti fraud machine learning system can take into account browser resolutions to flag suspicious values (as affiliate link fraudsters load content from sites with invisible iframes). It would be have been a stroke of genius for a fraud manager, but a cakewalk for a machine.
Similarly, algorithms don’t need breaks, holidays or sleep. Fraud attacks can happen 24/7, but even the best fraud managers might come to work on Monday morning with a backlog of manual reviews. Machines can ease up the process by sorting through the obviously fraudulent or acceptable cases.
How Exactly Does Artificial Intelligence Help With Fraud Detection
For a concrete example of how to use machine learning for fraud detection, let’s look at a business that processes online payments. Unfortunately, you’re bound to see many fraudulent transactions. But every transaction comes with data, including user behaviour data. That data goes through risk rules, which are calculated in real time. These rules give out a score, which can be analysed by data scientists or the risk team.
Where it becomes interesting is when a machine learning model begins suggesting rules, based on historical data. The machine learning algorithms can also work in real time to suggest which parameters to check, and improve the accuracy of the risk scores, which are then used to detect fraud and payment fraud.
Why Human Knowledge is Irreplaceable
Speaking of manual reviews, you always get these cases that fall into a grey area. This is where the best algorithms can’t help. You’ll always need human supervision to decide whether you are dealing with a false positive or negative.
Fraud managers also benefit from the insights into their industry. They can accrue external knowledge by reading up on the latest trends, and prepare their detection methods accordingly.
Last but not least, machine learning is worthless if it’s not pointed towards the right questions. Algorithms need training, and it also means gaining access to data to enrich their detection patterns. Only the best fraud managers will know what data is important, for instance, device fingerprinting, email or IP address, etc…
Benefits of Combining Both into a Hybrid System
With constant new attack vectors to fend off, fraud managers have the challenging task of keeping low overheads, fighting criminals, and justifying their decisions to executives. Which is easy to see why adding a machine-learning solution to their workflow can make things easier while giving them enough control:
- Ability to define custom rules: this isn’t something machine learning can do, but the right system should absolutely let them tailor rules to their own needs.
- Take advantage of whitelisting and blacklisting features: similar to the above point, not something a machine learning system can implement for them.
- Flag suspicious users who have not yet committed any fraud: based on their experience, the best fraud managers should be able to anticipate potential fraudulent attacks from otherwise unsuspicious users.
- Complete control over the system: one of the most important points for fraud managers is the ability to leverage automation without rescinding control over the system. A good hybrid solution should allow one without sacrificing the other.
- Freeing time and resources: fraud managers have a lot more to do than simply making yes or no decisions all day long. For banking institutions and many other industries, they must file paperwork to justify their fraudulent user flagging – automation frees up time to perform these tasks unburdened.
How SEON makes things easier for fraud managers
The SEON Sense platform is designed to be intuitive, collaborative, and effective. The Admin Panel offers an outstanding user experience, whether you’re new to risk management or a veteran in the field. It’s easy to add team members and to manage permissions for streamlined collaboration.
When it comes to the machine learning models, no need to know about data science. The rules come in human-readable form, which means you can understand and tweak them as needed for the best results.
While Machine Learning for fraud cannot exist in a vacuum and be implemented without the right supervision, it can certainly make things easier for fraud managers. With a fast decision time, better and more accurate overview of patterns and anomalies, an AI-based fraud system will free up a lot of resources.
In short, machines are fantastic at processing and memorising knowledge; humans are still better at applying it. This is why combining both insight and machine learning to fight fraud will not only improve your detection efficiency, but also help it improve over time.