Applications of Machine Learning in Finance
The financial sector has always been exceptionally fast-paced, with new data produced every nanosecond. As technology improves and the amount of data possible to store increases so too does the applications of machine learning in the industry. Whilst we find the industry is very rich in data, it is deprived of information. Information that would be available at its fingertips, if only there were machines intelligent enough to transform their wealth of data into insights. This is where machine learning enriches the scene and takes the game to the next level by giving observations previously unachievable. Could we predict markets better? Could we provide investors with smarter choices? Up to now, we have had limited human power to answer these questions. But with help from machine learning, the capabilities are pushed to another dimension. This is a current and tirelessly growing topic of research, with more and more benefits being uncovered.
Kraig Appleton is a machine learning and data science expert with a love of building and designing systems. He is a director at Credit Suisse where he leads the analytics team for Global Markets, trains staff, analyses proposed analytical approaches, designs and presents new ideas for the bank. Kraig is currently finishing a PhD in prognostics and diagnostics and his engineer’s mind combined with his determination to find “truth” within data render him a practical professional at the front of the industry. His experience in engineering systems for a variety of industries including manufacturing, food and beverage and energy generation just to name a few give him a unique perspective on systems and models developed in the financial industry.