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Payment Trends in 2017: Fighting Fraud Using Machines

09/01/2017


​​MNP's TAKE: The double-edged sword of technology means that not only has the speed at which business is completed ramped up, but also how fast cyber criminals can steal your assets. Automated tools to help prevent and detect fraud are becoming more sophisticated, and with machine learning, can review larger volumes of transactions faster than your human staff. The quicker fraud is detected, the smaller the loss.

However, it’s critical to remember humans are still the ones behind fraud. Collusion between two or more employees can defeat electronic measures, making the experience and expertise of a human critical to address potential crime. Transaction monitoring is best suited to fraud involving asset misappropriation; it is much less likely to protect against corruption or financial statement fraud. For more information on how MNP can help protect your organization, contact Greg Draper MBA, DIFA, FCPA, FCGA, CFE, ICD.D Vice President - Valuations, Forensics and Litigation Support, at 403.263.3385 or greg.draper@mnp.ca


BY SANDRA WROBEL-KONIOR FROM BUSINESS2COMMUNITY  

Machine learning can be used in many ways today and scientists are just limited by their imagination. Can the power of machines be strong enough to fight fraud?

Today, computers can not only read digits or text, but also understand the context, recognize the sentiment and can predict user’s behaviour. Machines can respond to us and that’s why there are more and more virtual assistants. Artificial intelligence chatbots are an addition to the customer service team as companies start to implement machine-based solutions to their everyday activities.

Machine learning is about trying to understand reality and make our life easier.

What is machine learning?

Machine learning is not as complicated as you might think. It involves teaching computers to learn for themselves. A special program teaches computers how to act in certain scenarios and how to perform complex tasks. It enables the machines to predict future outcomes. In a few words, machine learning is telling a computer how to solve a problem or what to do under certain circumstances.

Of course, identifying patterns and making predictions from patterns takes time. Scientists have used computer learning for over 60 years and continue to make it more effective and accurate. It’s getting more innovative, recognises a larger number of patterns and can solve more complicated problems. Machine learning has enormous potential and it can be used in every area of our life.

Machine learning is highly popular in the entertainment industry. For example, you experience it when using Netflix. Netflix sorts through large amounts of data to display film and TV series suggestions that you might enjoy. The same thing happens when you buy online. You can see suggested items based on your previous purchases. So, how can we use machine learning in fintech?

A huge opportunity to mitigate fraud

We hear about innovations in the online payments industry every day, but at the same time, fraudsters are using more sophisticated methods to steal sensitive data. You need to keep in mind that fraudsters are constantly changing their tactics to make their efforts more effective. And for online businesses, it’s getting harder to determine which transaction looks good and which one should be rejected.

Well-known fraud management systems based on rules require more manual reviews so the entire purchasing process could take longer. Today, while e-commerce and m-commerce is constantly growing, retailers have less time to detect fraudsters manually. Moreover, fraudsters are getting more clever and they are utilizing new technology to launch more complex attacks.

According to Global Fraud Attack Index, the cost of fraud continues to rise. At the beginning of 2015 less than $2 out of $100 was subjected to a fraud attack, but by Q1 2016 it was $7.3 out of every $100. The 2016 UK e-c​ommerce Fraud Report reveals that the top challenge for UK businesses is manual review and 45% of companies have problems with accurately measuring fraud metrics by sales channel. Businesses want to minimise manual reviews because of the costs.

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The truth is fraud is unstoppable so merchants need a strong system that detects suspicious transactions. Manual reviews are time-consuming and, especially when you run a large business with lots of transactions, you need a number of employees to manage it. It can be effective, but can also cost your business more than the fraud itself.

Streamline fraud review

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.

Machine learning is now used to prevent, or at least limit, fraud attempts. It works similar to our brain, but using technology. It isn’t perfect, but, in many cases could solve lots of problems. And it’s one of the best methods you can leverage to grow your loyal customer base. It’s extremely helpful with multi channel payments when your customers pay on your website and via mobile devices.

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.

Sometimes genuine orders may be rejected because they aren’t tailored to the typical behaviour pattern. But, keep in mind that a machine learns from every transaction so it could be more accurate in the coming weeks.

Machine learning is a great solution, especially for large e-commerce businesses when speed and scale are paramount. The speed of fraud detection should also come with a high accuracy level. The machine-based approach comes with smart automation and in the payment industry, it can be used to lower fraud attempts and make analyst’s life much easier. It also allows a look at more granular information a human being might miss when checking transactions manually.

The promise of machine learning is huge and, when done properly, could really help businesses grow and meet their goals. Computers are extremely precise, and with the larger number of patterns, the fraud detection will be more accurate. The more data that is collected across historical transactions of many clients and industries, the better precision in fraud detecting. In all, it comes with lower costs by minimizing the expenses of manual reviews.

Is there space for a human factor yet?

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 behaviour 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 behaviour 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 in SecurionPay.

 

This article was written by Sandra Wrobel-Konior from Business2Community and was legally licensed through the NewsCred publisher network.

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