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

Data Overflow

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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.

Resources:

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

Episode 7: How AI could save the planet’s biodiversity?

Data Overflow

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Innovations in Data Science and machine learning have brought the benefits of Artificial Intelligence to bear on our daily lives. By working alongside machines, people can now accomplish more by doing less. Putting AI into good use can help solve some of the world’s most urgent and difficult problems rather than helping organize our calendars, order our groceries, or play games.

One of the most fundamental global problems today is the exponential loss of biodiversity. In fact, scientists say that our planet is in the middle of its sixth mass extinction, the worst one since the extinction of dinosaurs, 65 million years ago. And that is due to countless direct and indirect human actions, such as poaching (illegal hunting or capturing of animals), overpopulation, habitat destruction, and climate change. Our planet’s rich biodiversity is taking such a huge hit. 

In this fight against human greed, AI models have turned into an unlikely ally, helping save our planet, paradoxically from our own hands.

 

Today, environmentalists are facing unfair odds as hunters call on the latest technology in night-vision goggles, military-grade weaponry, and sophisticated transportation. But thanks to the immense power of deep learning, AI has unlocked the ability to rapidly scan, process, and analyze a variety of signals, identify risks accurately, and provide almost immediate alerts to the authorities.

It is a type of AI system that is particularly effective at pattern recognition and identification. For example, when these models are given thousands of pictures of whale sharks, they can learn to spot a unique whale shark from a future sighting, with remarkable accuracy let alone handle unstructured data such as images, videos, and audio clips. This incredible feature can help solve another urgent matter which is species collection.

To this day, scientists have discovered and described only 1.5 million species of the estimated 10 million on earth. At current rates, we will have to wait almost 500 years to collect all the estimated species and by then, most of them may be extinct. AI and associated technologies have the ability to close this information gap cost effectively and efficiently with hardware becoming increasingly cheap and power-efficient enough to deploy monitoring systems on the ground, on animals, in the sky, and up in space. Early work is proving that algorithms can sift through the massive amounts of data streaming back from these monitoring systems. In turn, humans and machines can begin to identify the plants, birds, fish, and other species captured by these remotely deployed cameras, microphones, and more sometimes down to the unique individual. And we are finding new ways to deploy these technologies every day. For example, Microsoft is working on ways to use organisms such as mosquitoes as small, self-powered data collection devices that can help us better understand an ecosystem through the animals they feed on.

Artificial intelligence can help us understand land-use patterns as well. Microsoft and others are experimenting with ways to turn high-resolution imagery into land cover maps. These maps provide an unprecedented view of what is where, and how it is changing. This in turn helps governments, organizations, and researchers make more informed decisions about when, where, and how to deploy conservation efforts most effectively for the greatest impact. This creates a virtuous cycle of learning, as all this information can then be fed back into AI systems, making them smarter. Thus, AI methods make it possible to build a digital dashboard for the planet, allowing us to monitor, model, and manage environmental systems at a scale like never seen before.

The most obvious use of AI indeed seems to be for further extraction, consumption, and production. However, in the middle of a climate crisis, and with a deteriorating ecosystem, species are dying. Artificial intelligence can be a magical silver bullet that will help us restore the planet. It won’t be an easy journey, but by applying the power of AI to help both humans and our natural systems thrive, we can help provide a better and healthier future for the planet.

Resources:

How Artificial Intelligence Could Save the Planet

A Multilateral AI Strategy for Biodiversity and Restoration

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