Here’s the space for a machine learning technique that uses historical and live data to create patterns for customers’ behaviour. These patterns allow the system to make accurate fraud predictions.

Advanced algorithms evaluate every transaction for fraud risk and take appropriate action. The system creates deep profiles based on gathered data and analyses it to make the most accurate predictions and prevent fraud attempts. Machine learning integrates historical data with streaming information and is able to make the analysis in real-time. When a machine has more data, its accuracy will improve. To imagine how useful the machine-based process is, consider that a person could check just one transaction in about 5 minutes. A machine can check larger amounts of data in no time.

Machine learning, with a high degree of accuracy, makes it easy to leverage fraud scoring, which simplifies the way of detecting fraud automatically. Advances in data science make the business grow and scale better and can streamline the entire process.

So what is your role here? Are your actions still needed or maybe a machine’s knowledge is sufficient? Is there a space for a human’s insight or will computers replace us in the near future?

A machine learning approach includes manual reviews and it’s not going to change quickly. Even the most advanced machines can’t completely replace humans when it comes  to making effective decisions. As I said before, these are just machines so there will be situations when good transactions are flagged as fraud. Mistakes always occur, e.g. during the hottest shopping seasons when customer’s behavior could be different than the rest of the year.

Note that machine learning is based on input data which has to be relevant to identify a suspicious transaction. When we ‘feed’ a computer with inappropriate data, it learns wrong things which may cause irrelevant fraud score.

Add to all of this that fraudsters behave differently so it’s almost impossible to predict all their attempts. Sometimes it’s not easy to set suspicious behavior apart from genuine customers.

Machine learning is prepared for changes by analyzing and processing new data so the fraud detection models are continuously updated. The ability to process large data sets in milliseconds uncovers patterns in granular detail for a specific client. It helps you reduce manual work and improve the precision in fraud detection, but it won’t replace you completely. Your role is to review flagged payments to make sure if a specific payment is fraudulent or not.

Note that the machine learning algorithm is better when you deliver more accurate data. To make the system more effective, you need to spend a few weeks manually indicating whether each payment is fraudulent or not.

When you run an online business and process payments on your website, it’s up to you how user-friendly and hassle-free the payment process will be. You should do your best to offer customers the most innovative solution you can afford. Machine learning makes it dead-simple to detect fraud automatically. Real-time fraud fighting is essential for every business that processes payments on its website. It helps protect your revenue, as well as your customers’ data.

The costs of fraud and its related fees have a significant impact on your business and it could even bring down your company. For you, an online business owner, focusing on your product and customers is the most important. You shouldn’t have to be bothered with fraud issues.

The promise of machine learning is huge and we expect it to make more accurate results to effectively mitigate fraud. What do you think, can machine learning revolutionise fraud management?

This article originally appeared on securionpay.com