How Machine Learning is a Game Changer in Fraud Detection


One of the most exciting technology topics right now is machine learning. There are so many different applications including helping cut down on online fraud. The internet is a great place because of the convenience of having access to information, products, and services from our mobile devices. With that, however, comes many new and evolving risks, scams, and opportunities for fraud. Scammers target individuals and companies to get them to divulge personal and financial information. Millions of dollars are lost every year through these cyber-attacks, but machine learning is stepping up the game against such illicit activities.

Understanding Machine Learning

Before we dive into the applications of machine learning, let’s recap what it is. Artificial intelligence (AI) has permeated many of our daily uses of technology even without our realizing it. It’s impacting how we use our phones and even our experiences when using social media and the internet. One of the applications of AI is machine learning. This involves allowing computer and software systems to learn about a particular practice or procedure using complex algorithms and much data. From there, they can make decisions based on inference and pattern recognition. This is a shift from the way computers have functioned in the past because, with machine learning, they don’t have to rely on explicit instructions for all the tasks they must perform.

Using Machine Learning in Fraud Detection

One of the main challenges with e-commerce and making payments online is the security risk that your information might reach the wrong hands. Thanks to machine learning, it is becoming easier to detect fraudulent transactions and safeguard confidential financial information. Machine learning relies on having lots and lots of data. Many companies can use these systems to analyze the millions of online transactions processed on a regular basis. Any suspicious behavior can be weeded out quickly.

That’s exactly what payment giant, Paypal, is doing. It is using machine learning to help in its fight against money laundering. It compares millions of payment transactions to differentiate between those that are legitimate transactions and those that are not.

Rule-based anti-fraud computer systems have been effective at catching many transactions in the past. Take, for example, when your bank alerts you that your credit card has been used from halfway across the world. Machine learning takes this a step further. It can identify hidden fraud cases instead of only picking up on obvious ones. It can automatically detect suspicious cases without manual input, and this happens without delay, in real-time. Another major advantage of machine learning in fraud detection is that all the information gathered can be used to cut down on how many verification steps you have to go through before you can process your transaction online. After all, while you may appreciate the measures used to keep your account information secure, too many steps can steal your time and dampen your experience.

More Examples of the Benefits

Every industry handling thousands or millions of transactions and user accounts can benefit from advanced fraud detection procedures using machine learning. One example is the insurance industry. Car insurance companies handle huge sums of money. Everyday people submit claims, and these must be processes thoroughly before the money can be released. There’s plenty of opportunity for fraud. It could be through completely false claims about accidents that did not take place as stated, duplicate claims, overstated repair costs, or other falsified information. Companies can use machine learning to identify patterns and inconsistencies that could save insurance companies millions.

Other industries that can benefit include the medical insurance, e-commerce, credit card, and loan application sectors. There are a lot of exciting developments in the works to stop fraud and safeguard companies and individual users.

--- ADVERTISEMENT ---

reset password

Back to
log in