How Machine Learning Fits Into Financial Services

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By Joanne Graham in Machine Learning on Mar 13, 2018

While the rise of machine learning may make sense in many ways, how exactly it fits into the complicated and fast-paced world of finance may not be so clear. After all, while a machine may be able to learn from a given input, how can it take its cues from the relative freneticism of finance?
The answer is twofold, with the first part found in its ability to automate everyday tasks, which were previously done by people.  Prior to machine learning, the stumbling block for technology lay in the fact that these everyday abilities, such as recognising someone, were examples of tacit – or unspoken – knowledge, which were very difficult to translate into specific instructions for a computer. For example, it’s easy to imagine walking into a room and identifying a friend, but less so to explain the steps of that process; effectively, it was our inability to explain our abilities that prevented us progressing. This is actually known as Polanyi’s Paradox, after the philosopher Michael Polanyi who first described this conundrum in 1964. But thanks to advances in AI, machine learning now allows for the automation of tasks that, until recently, lay beyond the limits of available technology.

Secondly, it’s designed to derive insights, from the most esoteric of patterns to the biggest of Big Data, all of which develop and refine its own learning, and this unparalleled ability to generate its own progress means that everything from fraud-fighting to insurance underwriting is falling under the magic of the machine.
1 – Fraud analytics; its ability to identify anomalies in patterns of data means machine learning is being used as an extra layer of online security to secure and safeguard customer money, both by banks and retailers.

2 – Credit ratings; being able to almost instantaneously assess a credit application means quicker decisions for both customers and service providers alike.

3 – Banking personalisation; the drive to sell personally-tailored products to customers by banks and other financial institutions belies the fact that machine learning is doing the thinking behind the giving by analysing the financial footprint of each customer.

4 – Insurance; at its most cutting edge, tech start-up, Lapetus Solutions asks that its customers simply send in a selfie, from which their technology will derive all relevant information with which to build a life insurance quote, while over in the agricultural sector, drone technology is revolutionising crop insurance. Big data is also being used to help spot fraudulent claims in the car insurance industry, which in turn refines the process of providing quotes.

5 – Investments; the long-term impact of machine learning on investments is a growth industry all of its own, whether it’s the onset of ‘robo-advisers,’ –  digital platforms which provide automated, algorithm-driven financial services, or the ability to extract information from ‘unstructured,’ or text-based data, generated by social networks, which is a nascent financial force. Artificial intelligence is also making bold moves in the trading world, where it replaces fallible human feeling with rock-solid conclusions drawn from the (big) data to produce a more reliable yield. In fact, Eurekahedge’s ‘AI / Machine Learning Hedge Fund Index,’ which tracks the performance of 23 hedge funds has consistently outperformed other hedge funds since 2010, providing investors with an annual return of 8.44% over the six-year period, versus just 2.62% from their CTA / Managed Fund Index. [1]
Finally, the analytical capabilities of machine learning, particularly in the field of Big Data, means that important insights can be derived more quickly than ever before, leading to better investment decisions being made – and implemented – by machine learning to drive us towards a better financial future.



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