Machine learning trends in the financial market

The terms machine learning and artificial intelligence (AI) are essentially interchangeable, although some would say that machine learning is a subsidiary of AI. Having said that, it would be impossible to negate the impact that machine learning is having on our lives.

Some might argue that it's actually pervasive while others might say that the technological improvements, innovations and breakthroughs brought about by AI and machine learning are serving humanity for the greater good. An argument can be made for both parties. What is undeniable is that AI is here to stay and it is having a positive impact on various sectors of commercialism, including the financial market.

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Business prognostication

Business intelligence has been positively hard hit by machine learning with developments looking set to change the way information gets disseminated and used. Business prognostication uses big data to gather key information so that decision-makers in financial institutions can take efficient and proficient actions. The wave of using machine learning in this capacity will dramatically reduce and even completely eradicate the space for human error.

For instance, online trading which has become a lot more widespread in recent years, is one of the most unpredictable finance sectors. No one really know how currency pairs will react or if a the price of a commodity will rise or fall, although the latest market updates from CityIndex, and renowned online brokers is essential to follow on a daily basis. But now with machine learning, better estimations can be made. It's already something that can be seen in trading automation software. This type of software sifts through massive bundles of data and analyses previous patterns etc. to literally trade on the trader's behalf. One of the biggest contributing factors to the business intelligence fraternity is anomaly detection. This form of detection can make sound decisions without the infringement of false-positives.

Financial fraud

The ability to combat money laundering doesn't just have regulatory implications, but reputational ones too. To briefly digress, money laundering is the process by which someone uses a legitimate means to clean money gotten via illegal means. When online casinos started springing up in the mid 90s, they soon realised that syndicates were using their venues to clean dirty money and this was usually done in online poker rooms where a group would convene and 'play poker' and then cash out their legitimate money.

It's part of the reason that online casinos employ external auditing parties to make sure they're not entertaining any money laundering practices. However, as the tech to curtail such activities has advanced, so have the criminals who constantly seek out loopholes and this is where machine learning and AI can really step up to the plate. AI can improve existing models of detection and take into account that quite often money laundering involves a series of bank accounts. Algorithms can be studied in greater depth to seek out flows of money typical of money laundering activities. As machine learning and AI seeps further into the financial sector and its systems of fraud detection, quicker response times can be achieved and real-time feedback can be provided to customers.

If customers can be immediately alerted to any wrong-doing occurring on their account instead of after the fact, it not only improves the safety of their finances, but also helps the bank or financial institution to build a sense of trust with their customers. And now with blockchain technology is being realised for its prowess, it's sure to also figure into FinTech in helping to improve safety on all fronts.

Lesley Montoya

Update 14 May 2021