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Machine Learning: FinTech’s Secret Weapon Against Fraud

The fintech industry is a fast moving machine, with cutting edge technology providing the fuel that drives it forward. A fascinating component of this ever evolving vehicle is machine learning.

The Business Dictionary defines machine learning as the “Ability of a machine to improve its own performance through the use of software that employs artificial intelligence techniques to mimic the ways by which humans seem to learn, such as repetition and experience.”

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Quite a mouthful. In simple terms, it’s a category of data science that basically makes use of AI (Artificial Intelligence) to enable computers to ‘learn’ without being specifically programmed.

According to market statistics, the machine learning industry will grow to $12.5 billion by 2019. Machine learning engineers are therefore in high demand and Glassdoor lists data scientist as one of the top jobs when looking at salary, number of available opportunities and work satisfaction. Modern day institutions, like Byte Academy, provides courses that allow individuals to learn the necessary skills that will enable them to embark on this rewarding career path.

You might also be surprised to know how many applications make use of the technology. From your email provider using it to help identify and filter spam, to Google using it to show the most relevant content in search results, even in phone apps to predict strokes and seizures (Healint). One of the most common uses of machine learning is, however, in the detection and prevention of fraud.

Fraud is a global problem and the cost of it runs into the billions annually. International auditing firm and professional service provider, PWC, says that 36% of organisations globally experience some form of economic crime. Staggeringly, that’s over one in every three businesses affected. In addition, due to the rising popularity of fintech, the overall amount of transactions that banks and other financial institutions have to deal with is increasing at the same, rapid rate. On the other hand, the time it takes to complete a single transaction is getting shorter. Good news for customers but increased transaction volumes coupled with decreasing completion times put enormous pressure on current fraud prevention systems to get it right.

More and more people are adopting digital solutions into their lives, generating vast amounts of data along the way. This data has a lot of potential. A core strength of machine learning is identifying patterns in large volumes of data. It is therefore just as good at recognizing deviations from said pattern, ideal for detecting fraud.

As technology is evolving, so too are fraudsters unfortunately, finding ever more sophisticated ways of exploiting weaknesses in data for their own personal gain. The problem is that many traditional fraud prevention strategies are based on detection. That means once a form of fraud has been detected, only then will certain preventative procedures be implemented. By that time the culprits have moved on to the next job and the cycle repeats itself. With machine learning however, a computer can analyse the data, make a decision based on the input and alter the output. Advancements in the technology mean this can be done without human interference instructing it to do so, also known as ‘unsupervised learning’.

That doesn’t mean there’s no space for the human element in fraud detection. Supervised machine learning refers to the process where a random set of transactions is selected and then manually sorted into fraudulent or non-fraudulent. The information is then used to create an algorithm that will enable the computer to separate new records into either fraudulent or non-fraudulent.

There are many data points that an algorithm in machine learning will evaluate before ‘making’ a decision. This includes the past behaviour of clients (time, type of activity, location, etc.) to the dependability of suppliers.

Another benefit of using modern computers is their ability to process and analyse data much faster and in greater quantities than a human can. And as machine learning is based on the analysis of large volumes of data, they are able to detect and prevent fraud much quicker than our brains can. The processing speed in state-of-the-art software allows it to determine, in a matter of milliseconds, whether a transaction should be flagged as potentially fraudulent or not. Thus blocking the forged transaction before it’s completed.

Machine learning platforms used in the detection of fraud need to be responsive, able to adapt and, most of all, they need to be accurate. Providing security to customers is all good and well but when you start flagging ordinary clients who are going about their daily business (false positives), you run the risk of completely alienating a loyal customer base. Thankfully, similar to the increased speed mentioned above, computers tend to be much more accurate in processing extensive data sets than the human brain is.

Finally there’s the cost-benefit. Banks and other financial institutions spend millions of dollars on fraud prevention annually. It costs a lot of money to survey and monitor regulatory business aspects. The fact of the matter is that computer software not only makes this process faster and more accurate but, with improvements in machine learning, it can also make it more cost effective. Where organisations traditionally had to employ multiple employees to monitor transactions and corporate activity, they are now able to achieve the same result with one computer, equipped with intelligent machine learning capabilities.

Oakhall, a London based analysis firm, estimates that the overall saving due to machine learning could be as much as $12 billion annually.

Having said that, sceptics are concerned with taking away jobs from human beings to make way for computers and technology. it’s a concern we’ve heard many times before. However, there is an equal and opposite effect. The high demand for technology, specifically in the detection and prevention of fraud, leads to increased opportunities in the data science sector.

Through studying and mastering a multi purpose programming language like Python, and getting on top of data-modelling and evaluation, machine learning engineers are able to assist in tackling a global problem that cost businesses billions every year.

Bio: Follow Saloni Samant on Twitter, and visit her profile on LinkedIn.