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?
Any fraud lies in cheating people. It’s the ways of doing that that differ.
Here are the most common manipulations for financial fraud:
Detecting, eliminating, and preventing these threats are sore points for e-commerce and banking industries. Machine learning is their way out.
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.
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.
Let’s imagine you’ve trained the model, and now you need to know if it is working correctly.
