Fraud Detection Software: Leveraging AI and Machine Learning

When starting a business, no one thinks about the worst-case scenarios. But fraud is a very real threat that isn’t going anywhere – your best bet is to deal with it sooner rather than later.

So, how do you handle fraudsters who keep getting better at what they do?

The answer lies in switching from traditional to advanced fraud detection methods. As much as we appreciate old-school techniques, the truth is that they haven’t exactly paid off, leading most, if not all, SaaS platforms to introduce some form of AI and machine learning in their fraud detection systems.

Wondering what good will that do? Let’s dive in and find out!

Why advanced fraud detection is important

Simply put, it’s important because only advanced detection strategies can stand up to advanced fraud tactics. Fraudsters are no strangers to technology and will do everything in their power to find a weak link in your system. After all, exploiting vulnerabilities is their full-time job. 

This means that they’re getting better by the day, and so must we. By taking advantage of AI and machine learning, you can quickly adapt to new threats. Here’s how you can benefit:

  • More accurate detection: SaaS platforms that rely on AI and machine learning can spot fraudulent attempts more precisely. They help reduce false positives and negatives, which was one of the biggest challenges for traditional fraud detection methods. 
  • Increased adaptability: AI and machine learning have made it possible for SaaS platforms to learn from new data in order to adjust their algorithms. They do so continuously so that no fraudulent activity slips away. 

Advanced fraud detection systems are more effective in… well, detecting. However, you won’t only benefit in terms of technical aspects. You’ll also get to stay ahead of the game and enhance customer trust by making sure that their information and transactions are safe.

In case you have an overall challenge managing finances, you can always turn to QuickBooks for streamlined tracking and organization. It’s designed with small businesses in mind to help you make sense of your finances so that you always notice potential irregularities.

What’s more, platforms like Microsoft 365 and Dropbox can further help you improve your fraud detection strategy by allowing you to securely collaborate on finance documents.

AI and machine learning in fraud detection

Yes, some are repulsed by the thought of AI and machine learning, fearing that they could take over their jobs. However, AI and machine learning have been extremely kind to fraud detection platforms. They’ve allowed them to grow in a way that traditional methods could never. 

For example, sanctions screening used to rely on static databases, but now, by relying on AI and machine learning, fraud detection software like SEON can finally ensure a more accurate screening process. The same goes for identity verification, which has moved from manual checks to automated processes that provide faster and more reliable results. 

Let’s find out more about AI and machine learning’s benefits in fraud detection:

Detecting fraudulent activities in real time

Whether you detect a possible fraudulent attempt right away or after a few days makes a huge difference. By the time you’ve noticed that something is wrong, fraudsters could have already dug their claws in, causing irreparable damage that could hurt you and your customers.

One such example is Equifax, a company that, in 2017, fell victim to a data breach that exposed the personal data of 147 million people. They did sign a settlement with the Federal Trade Commission afterward to help those affected, but it would definitely have been better if they had prevented the breach sooner rather than having to deal with damage control later.

This is what AI and machine learning bring to the table. They help you detect any anomalies as soon as they occur. They ensure that fraudsters get no further than their initial attempt. 

Predicting future fraudulent activities

AI and machine learning are thorough in their analysis. If you have a record of thousands of transactions, these algorithms analyze each one, making sure that nothing is out of the ordinary. 

So, what do these analyses involve?

  • Behavioral patterns: Each one of your customers has their own way of interacting with your platform. With every interaction, they leave a digital footprint that AI and machine-learning-based systems analyze to detect possible anomalies. For example, unusual login times or locations or changes in their spending habits. 
  • Threshold monitoring: While this was around even before AI and machine learning came into the picture, it became much more advanced once they did. Now, SaaS platforms can easily set dynamic limits and monitor any changes in real time. 

Being able to continuously learn from new data, AI, and machine learning are far more advantageous than traditional fraud detection methods. They respond well to feedback, making sure that their algorithms are updated accordingly. This is why they’re never likely to fall behind.

But AI and machine learning don’t only allow fraud detection systems to monitor potential fraud more accurately. There’s more to the fraud detection landscape. They also allow platforms like Drata to better their compliance process so that you stay compliant with industry regulations. 

Reducing the number of false positives 

While it’s true that false alarms can happen regardless of whether you use traditional or advanced fraud detection methods, AI and machine learning help reduce them immensely. 

Why is this so important?

Because the last thing business owners need in their busy lives is spending time and resources on activities that are falsely flagged as fraudulent, per Anodot guide on false positives. It’s simply unnecessary. Not only does it frustrate you, but it also frustrates customers, as no one fancies being falsely accused. 

The problem is that it’s easy to mistake a non-fraudulent activity for a fraudulent one. A customer could have simply bought a new smartphone or moved to a new country, which would lead to an anomaly in their usual pattern. Reducing the number of false positives is a challenge even for AI and machine learning, but according to statistics, they can help reduce it by 96%. 

Allow for integration with decision-making systems

Fraud detection platforms can now easily use both real-time detection and decision-making systems, as AI and machine learning allow for it. These decision-making systems are responsible for taking immediate action in case a possible fraud attempt has been detected. 

All of the above-listed benefits wouldn’t have been possible without this integration. It’s what allows SaaS platforms not only to detect risks but to also mitigate them as soon as possible. 

Potential risks of AI and machine learning in fraud detection

Before I go over the possible risks that could arise when integrating AI and machine learning into fraud detection, I want to make it clear that the benefits really do outweigh the risks. But with such an impactful technology, you must be aware of any risks, no matter how small. 

  • Relying too much on AI: It’s only natural for people to use something that has the potential to make their lives easier. However, over-reliance on automation can sometimes hinder your processes rather than benefit them. These tools are great but not good enough to monitor and address fraud without any human intervention. 
  • Lack of transparency: While AI and machine learning can simplify processes, it might be too complex to understand how they actually do it. Because of this, you might fail to prove why you’ve flagged a transaction as fraudulent, leading stakeholders to resist. 
  • Adversarial attacks: As already mentioned, fraudsters aren’t going anywhere, and neither are their attempts. They can always try to trick the AI and machine learning algorithms through adversarial attacks, which, even though is hard to master, it’s still possible. This is exactly why you should never take fraud detection for granted. 

You see, AI and machine learning are continuously evolving, and as we adapt to them, these risks are likely to fade away. But until then, make sure to remain vigilant and up-to-date, as that’s the only way you can mitigate these risks and be aware of any changes in the industry. 

A step forward

After going over the benefits of AI and machine learning in fraud detection, we can all agree that this technology is a step toward a safer business environment. It has outdone traditional fraud detection methods in almost every aspect, so there’s really no point in holding onto the past. 

What you need to remember is that fraudsters will probably never stop finding ways to exploit businesses’ vulnerabilities, which is why you must never stop finding ways to protect yours. 

You’re not alone in the fight against fraud, and AI and machine learning are currently your biggest allies in fighting it. Once again – they allow for real-time detection, are highly scalable, can seamlessly integrate with decision-making software, reduce false positives, and can predict future fraudulent activities with ease. A true step forward, indeed.

Written by Makedonka Micajkova

Makedonka Micajkova is a freelance content writer and translator, always bringing creativity and originality to the table. Being multilingual with professional proficiency in English, German, and Spanish, it’s needless to say that languages are her biggest passion in life. She is also a skilled communicator, as a result of having three years of experience as a sales representative. You can find her on Linkedin.

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