A vital part of the business is how you detect fraud activity. Nowadays, it is more crucial than ever. The more data there is in the world, the more likely you will be a fraud target. Machine learning and artificial intelligence (AI) have been talked about for years, but it is now a reality within the payments industry.
Technology has made its way into several different fields. It starts from self-driving cars to detecting cancer. This blog will explore how artificial intelligence and machine learning can help detect fraud. It will explain how AI and ML work to identify fraudulent transactions and find our suspicious logins to the system.
Fraud Detection with Machine Learning and Artificial Intelligence
Before we get into the details of how machine learning and artificial intelligence work, let’s first look at what they are and what they can do. A branch of artificial intelligence is called machine learning. It is related to computational statistics and data processing.
Machine learning helps the algorithm adapt to new data. The advantage of machine learning is that it doesn’t need too much pre-programming. It can adjust to recent data, unlike some algorithms. It’s similar to artificial intelligence or AI. At the same time, artificial intelligence includes the ability to learn and adapt.
If your algorithm is not in the field, it is not in production. Deployment is a tricky topic in machine learning. It is not a field that most people understand well. Model deployment is an important milestone in applying machine learning models to real-world applications.
Automating the model deployment step is crucial. It allows rapid iteration of machine learning models to explore the model parameter space. Automation of ML models makes it easier for model deployment. The reason is that everything is done at the level of the infrastructure.
Thus, increasing the chances of finding a good fit. These technologies are constantly developing. We’re even seeing them in consumer products as simple as consumer cameras.
- Fraudulent Transactions
Machine learning and artificial intelligence are very useful in helping companies detect fraudulent transactions. Models are trained to predict if a transaction is fraudulent based on past transactions and other information. The systems look for patterns of fraudulent transactions. They serve as a preemptive measure against some kinds of fraud.
If a machine has categorized a transaction as fraudulent, then a human being is notified of the transaction. So, they will have the chance to re-examine the transaction and make the final decision.
If a person is making a transaction and purchasing it with a credit card, they will typically have to put in their signature for verification. However, if the signature is too far off from the signature they use normally, the machine can detect that something is off and stop the transaction.
Not only can this save the credit card company money, but it can also save the individual from being a victim of fraud.
- Suspicious Logins
Nowadays, people are always looking for new and better ways to verify their identity. With identity theft being an epidemic in the modern world, this problem is becoming more and more prominent.
There are, nevertheless, techniques to counter identity theft. You can employ technologies like machine learning and artificial intelligence. These technologies could detect suspicious logins and help fight against identity theft.
For example, if the login was performed on an unusual device, the user is accessing the login from an extraordinary time zone, or if the user uses a unique password, the system can automatically notify. Once reported, you can take the appropriate actions to suspend the account or get the user to confirm.
- Phishing Emails
As criminals get increasingly advanced with their attacks, the need to detect and block them becomes more and more vital. Since email is still the main method of communication in the world today, the number of phishing attacks is always on the rise, year on year.
To combat this, you must employ some of the best security measures to ensure no one person can compromise the infrastructure. Many companies have taken steps to combat this threat, but how can you know if you’re talking to the real person? Machine learning and AI have just the answer.
The machine learning algorithm is used to scan through the email to find any warning signs. This algorithm filters out the message and sends it to the human for further inspection. They can filter out phishing emails at a rate of 5-10 times more than humans.
Not only that, but it also filters out the false positives. It will allow you to take action against future phishing attempts.
- Fake Applications
Automated machine learning and AI algorithms are very easy to design. To detect fraudulent applications, many companies are using them. It will help if you feed your machine learning algorithm with a dataset of real fraud applications and a dataset of non-fraud applications. It will learn independently and separate the good applications from the bad ones.
For example: if the algorithm sees a bunch of applications where the number of emails, the number of languages spoken, the number of friends on Facebook, and the number of articles written are higher on average for the non-fraud applications. It will learn to rank the application as fraudulent when it sees these numbers more elevated than the average amount.
Detecting fraud is a serious business, especially with companies losing billions of dollars yearly to fraudsters. Fortunately, there are ways to prevent this, and companies are increasingly using machine learning operations and artificial intelligence to fight fraud.
Machine learning uses algorithms to teach computers to make decisions or predictions based on a set of data. And artificial intelligence involves the creation of computers that mimic human thought processes. While these techniques are relatively new, they are increasingly being used by many of the world’s leading companies to fight fraud.
There are multiple things a large company must do to set up a fraud detection system. First, you must choose and collect the data to train the machine learning algorithm. Then you need to set up the machine and allow it to introduce the data before you can start to gain accurate results.