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Machine learning is a relatively new and disruptive technology on the block. Various industry verticals are exploring their potential, the year 2019-2020 saw some key sectors adopting this technology for automation and business productivity. As per a report published by International Data Corporation IDC, the global machine learning market in 2017 was valued at $1.58B. This value was expected to scale up to $20.83B in 2024 with a CAGRof 44.06% between 2017 and 2024.
This study is an attempt to understand how some of these industry verticals have been leveraging machine learning to their advantage.
Banking & Finance
With digital transactions becoming the prevalent normal, the banking and finance sector is increasingly leveraging machine learning for securing financial transactions, risk management, and process automation. Financial trading companies are increasingly using machine learning for real-time predictions in the stock markets. Banks, on other hand, have been the early adopters of this technology for the very purpose of analyzing terabytes of data and detecting possibilities of fraud in real-time. According to Asita Anche, Head of Systematic Market Making and Head of Data Science at Barclays’ Investment Bank debunked the myth that machine learning is replacing the workforce. Rather, she expressed that, "it is taking repetitive jobs off our traders’ hands, freeing us up to spend more time focusing on delivering high impact results for our clients and our business.
The onset of the pandemic came with very important learning for this industry as it struggled to survive and fulfill the changing demands of the customer. Right from determining the apt pricing to predicting the consumer behavior, machine learning as technology has enabled both the brick-and-mortar as well as online stores to transform their supply chain processes in an ever-changing consumer-driven landscape. Let's take the case of e-Bay, the world's largest e-commerce platform. Since the e-Bay platform was used by buyers and sellers across the globe, there was an overarching language barrier to seamless business transactions. To mitigate this, e-Bay implemented ML based application called eMT (e-Bay Machine Translation) that facilitated 90% accurate translation of the product titles. This not only improved the trade but boosted the overall business by 10.9%. Evgeny Matusov, eBay’s Senior Manager of Machine Translation Scienceshared that “Machine translation can connect global customers, enabling on-demand translation of messages and other communications between sellers and buyers. It helps them solve problems and have the best possible experiences on eBay.”
The pandemic rightly re-aligned the human priorities and efforts at the same time. The machine learning models maneuver a humongous amount of data to derive meaningful insights much needed for clinical decision-making. One of the greatest use cases of machine learning can be found in diagnostics –Radiology and Pathology. Microsoft’s Project InnerEye has been acknowledged as a powerful AI platform based on machine learning that can successfully identity cancerous tumors using 3D imaging. The InnerEye team has been collaborating with Cambridge University Hospitals to test the efficacy of machine learning in cancer treatment.
From the above discussion, it is quite clear that Machine Learning is going to be one of the game-changing tools in the forthcoming years. This makes it all the more important for industry leaders to understand the implications of this technology and the frameworks through which it can be leveraged in different industry verticals. Professionals enrolling in the Executive Programme on Business Analytics, offered by IIM Calcutta in collaboration with Hughes, will have a dedicated module on machine learning, tailored to offer a comprehensive view of the technology, its models, and use cases for significant business insights.
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