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Fourthline, The new standard in KYC

Revenge of the nerds: How data scientists catch fraudsters (part I)

Konstantinos Leventis

18 min read·<br>Feb 16, 2021

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Image by Mcld under a creative-commons license. Reproduced in this article without any changes.Tony was woken up by thunder, early on a Tuesday morning. But no matter. The excitement about putting his sinister plan into action soon took over. It was a well thought plan, meticulously prepared. He would open a bank account with a made-up identity and a bogus passport he got from Sidney. Nobody’s better than Sid, he assured himself. With a bank account he could finally wash all that dough that had been piling up from pushing nose candy, and whatever else he could come by. He sat confidently behind the computer and begun the enrollment process. That was easier than I expected, he thought as he leaned back and hit the submit button, a minute or so later. But, to his absolute surprise, his application was rejected within seconds!<br>How could that be? How was his attempted fraud detected, with such apparent ease and speed?

In this and future posts we will explain how that is possible. In particular we will present and analyze various unsupervised-learning methods applied to fraud detection, in the context of KYC (know your customer), at Fourthline. After briefly making a case for unsupervised learning we take a closer look at some of the approaches we have tested. We summarize the results and draw comparisons between those unsupervised methods, highlighting their advantages, as well as their drawbacks, and conclude with insights that we have gained by applying them to the challenging problem of fraud detection.<br>Introduction<br>KYC is the process of verifying the identity and assessing the suitability of potential customers of, primarily, financial institutions. This is important in order to mitigate risks associated with the initiation of a business relationship between those two parties. Money laundering is at the forefront of the various criminal activities that KYC aims at preventing. According to The United Nations, it reaches annual volumes of about 2–5% of global GDP.<br>Press enter or click to view image in full size

As money laundering attracts more and more attention from regulators, the anti-money-laundering market has been growing substantially over recent years, according to market research.Opening a bank account is a major step in the money-laundering process and fraudsters go to great lengths in order to achieve that. The different types of fraud they (try to) commit can be roughly grouped into document fraud and identity fraud. From tampered documents to deep-fake videos, we’ve seen it all at Fourthline. What we have understood is that in order to be successful in tackling fraud, a financial institution needs to be able to identify all fraud types.<br>At Fourthline we take fraud prevention very seriously. We achieve remarkable results not just by having fraud experts employing thorough processes to ensure security and compliance, but also by leveraging their knowledge to combine it with state-of-the-art machine-learning algorithms. As modern digital banks understand, complementing human expertise with insights from data analysis and modelling is essential to be successful in a landscape of increasingly sophisticated fraud attempts and strict regulatory directives.<br>Machine learning<br>There are various ways in which machine learning can come in handy when detecting fraud. On the one hand there are computer-vision algorithms, commonly based on deep neural networks, and on the other, data-science methods, relying on heterogeneous features and a variety of data types.<br>On the computer-vision side, the authenticity of documents can be verified in pretty much the same way the human eye would go about doing it; by looking for elements in the image that support or undermine the document’s authenticity. Furthermore, biometric data (e.g. face recognition, liveness detection) play an important role in uncovering identity fraud.<br>Beyond images, though, there is a large body of data that comes with every application that a KYC provider, such as Fourthline, has at its disposal. From the time of the application, to the type of document and (mobile) device used by the applicant, there is always something to be learnt from historical data. Such data is what we use in order to train algorithms into powerful fraud detectors.<br>The cool thing is that these two disciplines do not have to remain separate. Results of computer-vision algorithms can always be expressed in numbers, representing confidence in, or probability of a particular outcome, or generally, a ‘score’. These scores can then be taken...