The past few years have seen a worrying increase in cybercrime, as both small and large companies have been targeted and have suffered important financial losses. And given the current context, where teleworking seems to be here to stay, businesses need to be even more prepared to deal with increasingly sophisticated cyber-attacks. So naturally, companies have turned to new technologies with the aim of finding advanced solutions to strengthen security measures.
And machine learning and big data analytics seem to be doing the trick. No matter the domain that they carry out their activities in, companies have found that implementing these two technologies, has helped them prevent fraud and detect such unpleasant incidents before they actually take place. But before getting into how these technologies manage to assist businesses in building stronger security-related strategies, we must first understand how they work.
So, let’s take machine learning, the subset of artificial intelligence that enables systems to learn from experience, analyse data and identify patterns, and make decisions without the need for human intervention. This technology can be applied in many areas, but when it comes to fraud detection and prevention, it truly fits like a glove. In this sense, machine learning algorithms are able to identify fraudulent transactions in a matter of milliseconds and with increased accuracy.
Thus, human errors are eliminated, while large amounts of data are being processed in real-time, so that suspicious activities are immediately detected and flagged. Moving forward, and as we have already mentioned, machine learning makes it possible for systems to learn from experience, so in terms of data, the phrase “the more the merrier” seems highly appropriate. This means that the more cases of fraudulent activities they analyse, the more efficient and easily it becomes for systems to recognize certain patterns and fraudulent activities, respectively.
Moving on to big data analytics, and what this technology implies, if it were to simplify, then we would have to say that this concept refers to the process through which businesses are provided with valuable insights, after large sets of data are examined. The information that companies are supplied with, by means of big data analytics, revolves around trends, customer preferences, and hidden patterns, that the human eye would find impossible to determine.
So one would already see how this technology assists businesses on their journey towards increased security, but just to name a few other elements that it includes, we must mention that big data analytics uses predictive models, “what if” analysis, and statistical algorithms, that all offer essential information. Key big data analytics tools also make use of data mining, business intelligence software, or artificial intelligence, with the help of which the best results are achieved.
So far so good, but how do companies integrate machine learning and big data analytics with the purpose of detecting and preventing fraud? Well, it all starts with data. Such solutions imply collecting large volumes of information, from various sources, therefore businesses have to resort to proper tools in order for them to be able to collect, store, and process such important amounts of data.
After the information is collected, whether from authentication or payment systems, databases, or other sources, it needs to be “cleaned”, refined and in other words, prepared for the next phase, which revolves around the addition of certain features, that will later on be fed to the machine learning systems. These features usually describe both a good and a fraudulent or suspicious customer’s behaviour, and can be related to data concerning orders, clients’ locations, email domain, or IP addresses, just to name a few. Businesses then establish a set of rules that the machine learning model will be compelled to follow, in the form of a training algorithm, and when the training is complete, companies will find themselves in front of a fraud detection machine learning model that is perfect for their needs.
Conclusion
Of course that there are many solutions for detecting and preventing fraud available out there, but none quite as efficient as those that include machine learning and big data analytics. And businesses that operate in various industries, such as finance, healthcare or e-commerce, have already begun to include fraud detection and protection systems, that are powered by machine learning and big data analytics, within their companies. So it’s safe to say that there is a bright future in store for the two technologies that fight cybercrime and put CEOs’ minds at ease.
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