AI future technology and business concept showing how machine learning can fight financial fraud

How Machine Learning Fights Financial Fraud

We have e-shops, online banking, online insurances, and tons of other online services.

But there’s one more thing we have – online fraud, as powerful as ever.

Fraudsters take advantage of any weak spot they find to steal millions before security teams can see and patch up the breach.

So companies are forced to look for new solutions to prevent, detect, and eliminate fraud. And machine learning seems to be the best answer to financial fraud.

How does it work, what are the benefits, and who uses it?

What exactly is financial fraud?

Any fraud lies in cheating people. It’s the ways of doing that that differ.

Here are the most common manipulations for financial fraud:

  • Identity theft – Fraudsters carry out transactions using someone’s identity;
  • Phishing – Collecting personal information (usernames, passwords, credit card numbers) through emails or illegitimate websites; and
  • Pharming – Directing customers to fraudulent websites.

Detecting, eliminating, and preventing these threats are sore points for e-commerce and banking industries. Machine learning is their way out.

Why machine learning?

Let’s start with the basics.

Machine learning (ML) is one of the areas of artificial intelligence. It’s the science of getting computers to learn and act like humans do and improve their learning over time in an autonomous fashion.

What does it mean? That means ML is perfect for detecting frauds! Its algorithms learn to tell fraudulent transactions from legitimate operations. ML fights financial fraud by using big data – better and faster than people do.

How does machine learning work?

First, the ML model collects information – analyzes the data gathered and extracts the required features.

It’s simple: the more information the model gets, the better it works.

Second, features that describe customers’ behavior are added, both good and fraudulent. Mostly, it’s information about users’ location, orders, network, payment method, and identity.

Then, a training algorithm is launched. In plain words, it’s a set of rules the model follows when telling a legitimate operation from a fraudulent one.

Finally, a bank or e-commerce organization gets a fraud detection model tailored to their company needs.

Still, companies can’t use one machine learning model for years. Fraudsters are getting smarter and come up with new tricks all the time, so the system should be kept up-to-date.

Is it working now?

Let’s imagine you’ve trained the model, and now you need to know if it is working correctly. How do you figure this out?

Show the model the data it has never seen before and which is 100% fraud. If the model recognizes the threat, you start deploying it.

Here are some fraudulent situations the model should react on:

  • Customer placing lots of high-value orders like jewelry;
  • Customer adds 10+ payment cards in an hour;
  • Account name and the name on the card don’t match;
  • Suspicious email address; and
  • Orders from a fraud location.

All these situations should be marked as fraud, so make sure the model marks them.

Why use machine learning?

ML beats the traditional ways of detecting fraud. It’s faster, works with massive amounts of data, and doesn’t depend on human resources.

What else?

Works better than humans

Machine learning algorithms are way more effective than humans. ML processes data faster than a team of the world’s best analysts ever could.

Besides, ML algorithms spot patterns that seem unrelated or go unnoticed by staff members.

As the algorithms study tons of fraudulent behavior cases, they detect even the most stealthy patterns.

More effective than traditional systems

A traditional fraud detection model heavily relies on human labor. These systems have been working for a long time, but they’re unsuitable for the digital era.

What’s more, if you rely on human resources, you may want to gather a team of top-skilled analysists. But top specialists are always expensive, and their skills and experience are limited.

Machine learning beats traditional detection systems in terms of speed, quality, and lower costs. Besides, as business data becomes more and more complex, analysts can’t cope with the scale.

Deals with overloads

To get ahead of fraudsters and their high-tech tactics, companies need to analyze more and more information. The faster the analysis goes, the more likely the company will detect the fraud.

And let’s face it: even if you hire the world’s best data scientists, they can’t determine fraud attempts as soon as they happen.

But machine learning can do that. Its algorithms work 24/7 and process an enormous amount of information with the flip of a switch.

Using big data for #machinelearning, algorithms can learn to tell #fraud transactions from legitimate operations. #AI #respectdataClick to Post


As you can see, machine learning can be very helpful when it comes to fighting cybercrimes. ML helps to prevent severe attacks on users’ and companies’ finances. It’s a fast, up-to-date, and cost-effective way to protect customers and the company’s data. No matter how massive is this data.

So, is machine learning worth the candle? Yes, it is.