How is machine learning transforming the finance industry?
The influence of machine learning, a strand of artificial intelligence (AI), is spreading through various industries. Over three-quarters of businesses are now pouring money into “big data”, setting the stage for machine learning to grow much further in reach over the next few years.
With the considerable amount of “big data” currently in their possession, financial institutions are poised to particularly strongly benefit from the emerging revolution in machine learning.
Machine learning is not just available to large organisations
Machine learning builds upon a range of existing disciplines. Econometrics, computational statistics and pattern recognition are all furthered with machine learning, and The European Financial Review suggests that every single aspect of a bank’s operation could benefit.
Some banks both large and small are already incorporating machine learning into their processes for risk management analytics. Furthermore, unlike highly capital-intensive information technology systems, machine learning can be applied by virtually any bank – not just established organisations.
Machine learning can thrive as digital infrastructure improves
Today’s finance departments can, due to recent advances in technology, more easily implement intelligent, connected, responsive and predictive digital infrastructure. If your financial firm already has such infrastructure in place, you have formed a strong foundation for machine learning.
You also could already be ahead of many other businesses in your sector. According to a survey mentioned by Digitalist Magazine, almost a fifth of small and midsize enterprises are still not using accounting software, depriving them of the ability to initiate more sophisticated solutions.
A little less administration, a little more automation, please
Many finance professionals continue to be regularly occupied with manual administrative tasks. In fact, finance professionals still spend merely 17% of their time pursuing strategic endeavours, with scant automation thought to be dragging on efficiency in their workplace.
However, as more finance teams forgo spreadsheets in favour of digital and cloud ERP solutions, they can use machine learning to automate back-office processes. As such processes as order-to-cash and procure-to-pay are handed over to automation, more of professionals’ time is freed up.
Add a machine touch to financial statement auditing
In the finance sphere, certain processes still rely on a degree of human input – even when machine learning is readily available. These processes include financial statement auditing, where humans must still be called upon to evaluate circumstantial reasoning concerning data.
However, through picking up on patterns in data sets, machines can detect possible discrepancies as well as verify conclusions reached by humans. Machine learning technologies are also capable of exponentially outpacing humans when sorting through high amounts of financial data.
After looking through this data, the technologies can turn it over to humans, who can tackle more abstract tasks – such as judging whether particular patterns or anomalies might warrant concern.
Identify abnormal patterns hinting at security breaches
CFOs are increasingly concerned about cybersecurity – and understandably so, given that more and more financial data is becoming stored and managed through digital means. Whether these digital solutions are on-premise or cloud-based, access portals must be monitored and regulated.
This is another situation in which machines’ ability to analyse huge sets of data can come in useful. In this instance, the machine would be looking for abnormal occurrences in how the data is accessed and used for transactions. Cybersecurity professionals can then consider how to act on the findings.
Settle on wiser choices in stock market trading
The common perception of stock trading floors – as propagated by film and TV depictions – is that of frantically waving and shouting men in front of screens recording rises and falls in share prices.
However, the real picture is rather more sedate, as roughly three-quarters of NYSE and Nasdaq exchanges result from computer algorithms, The Scotsman points out. These algorithms could be further enhanced by machine learning in finance.
David Richardson, director of partnerships at the AI-focused Bayes Centre of the University of Edinburgh, explains: “With lots of data you can build models which allow you to spot patterns. There is a lot of work going on in how you can predict future stock prices.”
Cloud-based predictive modelling
If your financial firm has a public cloud strategy, you could incorporate machine learning into it – about 66% of organisations already intend to do so by 2020. Following their lead could help you to recognise the best deals, assess the market impact of our trading decisions and judge credit risks.
A company like RedPixie, which specialises in building cloud solutions for financial companies, could transfer your Excel-based predictive modelling efforts to the cloud.
