In the last few years, all of us have watched closely as the coronavirus accelerated the transformation of commerce from physical to digital. Undoubtedly, e-commerce is one of the most affected sectors in this process. The convenience of meeting our needs with a single click, without leaving our comfort zone, is expanding the scope of e-commerce day by day. There are hardly any products that we can no longer buy online. But of course, there is another side to the situation. As the volume of transactions on e-commerce platforms increases, it is becoming increasingly difficult to discriminate between legal customers and fraudsters.
The complexity and high volume of transactions make powerful fraud detection methods a must for e-commerce merchants. Global loss to e-commerce fraud was $32.39 billion in 2020, and it is expected to reach $40.62 billion in 2027 with a 25% percent increase. Unfortunately, fraud methods are also rapidly adapting to digital. Fraud methods cannot be fitted into a single pattern by their nature, so fraud detection and prevention methods must be planned according to business and industry needs. Then, from where should you start? Should you immediately hire a fraud analyst, or can you use a fraud detection tool instead?
Below, we look at the value human analysts bring to the table, even in the era of powerful algorithmic technology.
What Value Can a Fraud Detection Tool Bring to Ecommerce?
Payment fraud methods are getting more and more creative. When starting a business, manual control may seem sufficient. However, as the volume of transactions increases, it will be harder to control all transactions manually along with various other responsibilities. The big and complex data will need to be sorted and classified somehow. It will surely be time-consuming and inefficient to organise a huge pile of data daily and search for suspicious transactions after a point. Therefore, you might consider integrating a data management system into your e-commerce business that organises and analyses big and complex data in real-time for you.
Luckily, there are many rapidly developing fraud detection and protection technologies against fraudulent methods that are changing day by day. Especially artificial intelligence-powered fraud solutions can greatly contribute to your big data management and analysis processes. By detecting complex connections between data that cannot be examined with the human eye, these tools help you have more precise insights and make data-driven decisions. As a result, with the help of more accurate insights, you can reduce the false-positive rate, avoiding customer friction and enabling your risk teams to focus on real risky cases.
By training your AI (artificial intelligence) model with test data and previous transactions, you can detect fraudulent activities that you have never encountered. And also, with the AI models with self-learning capabilities you can automatically prevent these orders from being accepted in the first place. Although it is very difficult to manage big data manually, it is your biggest asset when integrated with the right technologies. Artificial intelligence technologies, which can make connections by making inferences from previous transactions and data, can present you with millions of risky situations and connections that you cannot detect with manual examinations. The more data you train a machine learning system with, the better insights they will give you. Therefore, with a fraud detection tool that has these abilities, you can not only detect fraud, but also prevent it by discovering it before it happens.
While these systems sound great for taking your business to the places you dream of, they cannot fully operate on their own of course. Smart software needs smart hands to manage them. There are gaps that computers can’t fill. They can do the algorithmic work, but they can’t manage your crisis scenarios. Some of them are highly trustworthy, but they are what they learn. For instance, they cannot include external data that isn’t in your system in analysis.
What Can Your Fraud Analyst Offer?
Traditionally, a fraud analyst’s role is not very different from a fraud detection tool’s. The main responsibilities of a fraud analyst are monitoring and detecting fraudulent activities day by day. Each company’s procedure in case of detecting a fraudulent transaction may be different from each other. However, if there is a fraud analyst then it is their responsibility to catch fraudulent transactions or attempts of fraud.
In most cases, fraud analysts benefit from helpful software in their work. To detect fraud, you should be able to classify heavy transaction data first. Then, you have to match events with suspicious activity patterns and check each event one by one. If the transaction traffic is light, then it may be doable. Yet this is probably not the case.
The biggest challenge of an unassisted fraud analyst starts right at this point. At a certain point, it iis almost impossible to manage big data with human capabilities. Even if you work with a data provider, your analyst still needs support to properly handle the data you receive.
Furthermore, when you hire a fraud analyst, naturally you would expect them to be on top of all transactions and related processes. But for 24/7 ecommerce, that is not a very realistic expectation. This requires 24/7 analysis, something that is difficult for a human team to accomplish (much less an individual), but that is easy for a computer.
So Which One to Choose?
As our comparison shows us, both a fraud analyst and one of the best fraud detection tools have their advantages and challenges. However, if you have the chance to have both, then you have a dynamic duo. A fraud analyst can strategize, and the tool can apply that strategy almost perfectly, since machines aren’t prone to human mistakes. A fraud detection tool can always be on alert, unlike a fraud analyst. In case of suspicious activity, you can use the tool to prevent the transaction from being accepted until your analysts can review and make a final decision. A solid fraud analyst can use the data analysis performed by the tool to detect new scams, or decide the transaction is valid.
Ultimately, rather than evaluating these two abilities separately, the best solution is to combine the two in order to maximise their strengths and cancel out their weaknesses.
Editors note: This article was contributed by Arzu Özkan at Formica