Fraud Detection with Machine Learning is possible because of the ability of the models to learn from past fraud data to recognize patterns and predict the validity of future transactions. In most cases, it is more effective than humans due to the speed and efficiency of information processing. Machine learning allows for creating algorithms that process large datasets with many variables and helps find these hidden correlations between user behaviors and the likelihood of fraudulent actions.
Use AI and ML for Real-time Fraud Detection: Part – 1
Types of Internet Fraud and How to Prevent Them
- Document Forgery – Fakes IDs are available on the eCommerce market too, which can cause a lot of issues for the owner of these Ids. Machine Learning can identify the forged identity.The algorithm has trained its neural network to differentiate between a fake and original identity, thus creating a full-proof system.
- Email Phishing – This is a kind of cybercrime where fake sites and messages are advertised to users, asking them to share personal data. If a person is not too careful, he or she may enter any confidential data which can make them vulnerable to threats. The best way to avoid this fraud is for the user to be careful themselves, however, AI can do the job of finding out fraud emails by filtering them using basic machine learning algorithms like regression methodology.
- Credit Card Frauds – It is the hijacking of your credit card details. While you are entering the details into a particular form online, the hacker would be ready with their tools to hijack the information and use it elsewhere.
This can be detected by the Machine Learning algorithm added to your website. It will secure the information and ensure that the data is not given to the attackers.
- Mimicking Buyer Behaviour – This is the new kind of fraud, where the criminal studies the buyer’s behaviour and tries to imitate that. An in-depth understanding of the data can give Machine Learning the difference between the actual buyer and the fraudster. Identifying the location spoofing details, knowing where the fraudster is making these purchases from, and other details need to be added to the ML algorithm for better and accurate results.
- Identity Theft – This is another kind of fraud that needs to be brought to notice. In this case, the criminals tend to rob your identity connected with the bank accounts. They will change the IDs or the passwords, thus preventing entry into these accounts.
Machine Learning will ensure that nobody can change the password or update the identity associated with an account. As soon as anyone tries to hack into your account or plans to change the details, you will be notified. Two-factor security and other measures, along with human-like intelligence, help assure better prevention of frauds. - Advanced Software – Experienced hackers tend to use advanced anti-piracy and detection software, which can prevent regular browsers from recognizing them. They will create virtual IPs and machines, which allows them to commit the crime.
Machine Learning algorithms need to be fed with this data that can help them identify virtual IPs, machine anomaly, and fraudulent behaviour. As a result, you can save the payment gateways from being crashed by frauds.