Fraugster: A Startup that uses AI to Detect Payment Fraud

Payment fraud is what happens when someone cheats someone else in the course of conducting a transaction. For example, a person who makes a purchase using a stolen credit card is committing payment fraud. However, so are people using counterfeit bills, writing a bad check, and returning products to a store that they were not purchased from. As a result, payment fraud should be considered something of a catch-all term because it does not describe a single crime in particular so much as a wide range of criminal activities.

How Are Businesses Combating Payment Fraud?

Given the sheer range of criminal activities that fall under the catch-all term, it should come as no surprise to learn that payment fraud is a serious issue for businesses. After all, businesses can suffer significant losses because of such incidents, which are made all the worse by how they are often accompanied by a great deal of wasted time and effort that could have been put to much better users elsewhere. Combined with potential knock-on effects such as a fall in morale and a fall in sales from the consumers’ lack of trust, it is no wonder that businesses are pouring so much of their resources into fighting payment fraud.

With that said, the measures used to fight payment fraud are as wide-ranging in nature as the criminal activities that fall under the term. Some examples are simple and straightforward, as shown by how some businesses will train their staff to recognize counterfeit bills so that they can foil fraudsters at the check-out. In contrast, other examples are much more sophisticated in nature, as shown by the existence of counterfeit bill detectors that do nothing but detect counterfeit bills. This has become more and more important in recent times because payment fraud is becoming more and more sophisticated with the increasing digitalization of society, which in turn, means that the measures used to fight it are becoming more and more sophisticated as well.

What Is Fraugster?

There is no better example for illustrating this fact than the existence of Fraugster, which is a German and Israeli startup founded for the purpose of using AI to fight payment fraud. In brief, Fraugster started up in 2014 as a collaboration between Max Laemmle, who has previous experience as a co-founder of a payment gateway company called Better Payment, and Chen Zamir, who has previous experience in both analytics and risk management. The duo states that they founded Fraugster because existing techniques and technologies for fighting payment fraud were too outdated, which is why they decided to create an AI-based solution that could make decisions as fast as a human analyst but still retain a machine’s inherent scalability.

Whatever the case, it is clear that Laemmle and Zamir have chosen something with real potential, as shown by their claim that they are responsible for handling close to $15 billion in transactions for thousands and thousands of international merchants and payment service providers. Something that serves to explain how they have managed to secure another $5 million from their latest round of funding from both new and existing investors, which will be used to further grow their revenue-earning operations.

How Does Fraugster Combat Payment Fraud?

Fraguster’s AI functions by collecting relevant information before using it to make a decision about a whether a particular payment is fraudulent or not. Some of the information are seemingly mundane but nonetheless important factors such as name, email, billing address, and shipping address, whereas other information is much more complicated in nature, with examples including but not limited to email name match, IP connection type, and the distance between key strokes. In total, there are about 2,000 factors, which is a great deal of information that can be processed to produce a better judgment in much less time than what a single human analyst or even a full team of human analysts could do.

However, the core of the AI is the algorithm that is used to analyze the relevant information before making a final decision about whether a particular payment is fraudulent or not. In brief, it makes use of machine intelligence, which is when software is capable of learning from its experiences in much the same manner as living organisms but still falls short of genuine intelligence. As a result, it is not just capable of matching a human analyst’s results while using much less time, it is also capable of improving itself over time by learning from each of the payments that it examines as well as each of the decisions that it makes. Something that provides it with an enormous advantage because it means that it is at less risk from being rendered obsolete as it is on an automated path of constant self-improvement.

In other words, Fraugster can be considered another example of how machine intelligence is changing the fundamental manner in which a wide range of fields function. While it falls short of the true AI that is so popular in science fiction, its increasing adoption still means a wide range of changes that will have widespread consequences for the world, meaning that interested individuals should keep a close eye on what is happening lest they find themselves caught flat-footed by something that they didn’t see coming.

What Does This Mean For the Future?

Regardless, since Fraugster’s machine learning means that the AI can continue to improve its function as it continues to operate, it seems probable that it will become better and better at something that has needed human analysts up until this point. As a result, it seems like it will have a notable impact on the incidences of payment fraud in the world, particularly since its mechanical nature means that it can handle a much larger volume of transactions than what even the biggest and most prepared team of human analysts in the world can manage at the same time. However, it is important to remember that people can be surprisingly innovative when it comes to coming up with new ways to enrich themselves at the expense of others, meaning that it is too soon to celebrate. Still, if the people behind Fraugster handle it as well in the future as they have done so thus far, it seems probable that they will be able to tackle those issues when the time comes as well as they are doing now.

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