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Episode 3: Machine learning revolutionising the finance industry by improving fraud detection

Data Overflow




In this age of digital technology, fraud techniques are getting more sophisticated every day and the magnitude of financial fraud, especially, is becoming wide-ranging. A very recent study done by FICO found that 4 out of 5 banks in their survey have experienced an increase in fraud activities and this is expected to rise in the future.

Before diving deeper in the problem, let’s take a brief definition of financial fraud. It is mainly a crime of deceiving people in their financial transactions, aiming to produce financial gain for a business or an individual.

Whether you’re practicing identity theft, or attempting to take funds or other assets from a financial institution or from its customers, you are making a fraudulent transaction.

Due to increasing fraudulent transactions taking place through credit cards every day, business organizations are losing up to 5% of their revenues. A 5% fraud may not sound a lot, but in monetary terms, it is non-trivial. In fact, the firm will not only have direct financial loss but inaction will also generate a loss of market confidence.

In the past, in order to face these problems, the standard practice was to use the “Rule-based approach”. These solutions were deployed in SQL or C/C++ and it’s rather a long process that fails to treat the new techniques criminals use in fraud, and results in a high number of false positives(the machine is likely to block a lot of genuine customers). Moreover, as the firms grow bigger, trying to make some updates to the code led sometimes to the breaking of the entire codebase. We can say that this technique was able to only partially and slowly mitigate the problem.

In the same context, Companies were fed up of these inefficient solutions that didn’t prevent losses. As a result, they decided to bring in data scientists in order to rescue them out of losses. And that’s where Machine Learning interferes.

A variety of machine learning-based methods was proposed, both supervised and unsupervised, to tackle the issue of fraud detection using large datasets.

The supervised approaches treat the problem as a classification one. They rely on explicit transaction labels: ML engineers show machines, repeatedly, what genuine transactions look like during training to be able to distinguish the fraudulent ones later. Observations are classified as “fraud” or “non-fraud” based on the features in those observations.

Concerning unsupervised models, they are trained on unlabeled datasets to capture normal data distribution. And then, when given a new data instance, they try to determine whether the sample is legitimate or suspicious, based on the patterns and structures they derived.
The machine learning model being chosen, depending on the severity of the discovered “fraud-like” patterns, a transaction can be allowed, blocked or handed over for a manual review. This process called data scoring is done in milliseconds.

During a manual review, if a fraud detection team member marks the suspicious transaction as a legitimate one (false positive), Machine Learning model will take this information into account to make a better, more accurate decision next time. It means that the machine’s efficiency is increasing by time.

Thanks to Machine Learning, fraudulent activities were spotted most of the time and the process is very fast, scalable, efficient and more accurate than previous approaches. It’s now clear that Artificial Intelligence provides risk managers with a more powerful tool to do their job!

Unfortunately, Fraud cannot be completely eradicated for there will always be people with bad intentions. However, we’re hoping that by the reinforcement of financial systems with AI fraud detection tools, it will become significantly more difficult for fraudsters to make their “art”. With each new fraud technique, a new vision from the machine is born, improving its way of detecting suspicious patterns. And this is one of the best things machine learning can offer : it’s called Model upgrading and it means that just like in real life, where humans constantly improve their intellectual capabilities, models are doing the same.


Machine learning for fraud detection

A Beginner’s Guide to Machine Learning in payment fraud detection

Fraud detection: the problem, solutions and tools

Solving Financial Fraud Detection with Machine Learning Methods

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Sciences et technologie

Perseverance Rover: Humanity’s newest feat





February 19th, 2021 marks a new historical event in the space exploration journey, as NASA scientists rejoice.

NASA’s 2020 Mars mission successfully landed on Mars. This mission includes the Perseverance Rover along with other experiments.

While descending to touch down on the red planet, we got a close-up picture of the Perseverance rover, the first-ever high-resolution color image to be sent back by the Hazard Cameras that is most likely to become a classic photograph in the history of spaceflight.

A 360° rotation of its mast allowed the Mastcam-Z instrument to capture its first panorama, and we also got the first-ever audio recording from the red planet thanks to a microphone on the rover.

The camera system covered the whole landing process, showing the intense ride and the so-called « seven minutes of terror » descent sequence.

“Touchdown confirmed! Perseverance is safely on the surface of Mars, ready to begin seeking the signs of past life.” Said NASA engineer, Swati Mohan, during the live stream of the landing.

This landing is a considerable achievement of engineering that took multiple years to attain and was one of the main difficulties of the mission. As a matter of fact, because Mars’s air is so thin, it was more difficult to slow the spacecraft while descending, and it required a parachute and rockets, among other equipment.

Another challenge that the rover faced was the dangerous terrain it was headed to. Jezero Crater, the new home to the Perseverance Rover, was riskier than previous missions, as it presents deep pits, high cliffs, and big rocks making it harder for the rover to touch down.

However, NASA managed to design new pieces that allowed Perseverance to scan the surface and navigate around any obstacles which was a huge success.

The main goal of this mission is to hunt for the remnants of life and to study the Martian rocks up to 3.8 billion years old.

With this new source of data, the knowledge about our neighboring planet will pave the way to the future, when humans will set foot on the Martian soil.


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