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Machine Learning in Investment Banking

Written by CFTE’s Experts

Machine learning, a subset of ‘Artificial Intelligence’, has its roots spreading out to almost every domain related to work or business today. Machines replacing manual labour with automated technology has become customary for investment banking, and how UBS has implemented machine learning in the bond marketplace is just one example that makes us see the bigger picture. 

Companies using Machine Learning in Investment Banking

Apart from UBS joining hands with Overbond to bring machine learning to the table, there are tons of other instances where companies are doing the exact same thing to revitalise their business.  

We’ve got names like BNP Paribas—the world’s 8th largest bank by total assets—utilising machine learning by incorporating an AI-based tool of their own called the Smart Chaser. For investment managers, this technology can mean a whole lot of convenience as it allows them to automate trade processing by using predictive analysis.  

As a trade-matching tool, Smart Chaser helps compare the details of the client and broker both effectively and predict whether the trade is going to be successful or not. In turn, this saves wasted time and gives rise to being more productive.  

Speaking of the UK’s very own banking giant, Barclays’ vision is to become “the most AI savvy workforce in the UK.” Using their very own machine learning technology, they handle payments and rely on AI to make their trade decisions for them.

 

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Man AHL is also a well-grounded name in the area of investment banking. This is a company that consists of researchers and traders that develop investment strategies. Man AHL makes the best of machine learning by setting up AI trading platforms that route trades efficiently and in a cost-effective manner.  

Another multinational tycoon that acknowledges the fruit of machine learning in investment banking is JP Morgan Chase. From asset management to quantitative solutions that provide insights and data, this enterprise has AI doing the majority of the work for them.

Applications of Machine Learning in Investment Banking

The following are some noteworthy applications of machine learning in investment banking. Let’s take a look. 

– AI in banking has created more jobs than it has taken away. The demand for tech-savvy investment banking professionals is now surging more than ever.  

– AI-based tools such as Sigmoidal can be used for web-scraping for the automation of data collection. 

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– Machine learning’s predictive analytics has been a real game-changer in investment banking by helping companies determine possible outcomes during trading.  

– Katana is an AI tool of a Dutch financial company called ING and it has been made to assist bond-traders to make better decisions when purchasing bonds for their clients.  

– Machine learning can help with anti-money laundering (AML) more proficiently by detecting any acts of suspicion from customers in investment banking. 

The Future of AI in Banking

Over time, technology is bound to replace even the smallest aspects of our life. Banking, for one, is something large-scale and the incorporation of AI in this field is seemingly inevitable because it’s only going to grow from here on. To make machine learning as beneficial as possible, it ultimately boils down to us to utilise its full potential and negate all shortcomings with technical dexterity. 

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