Investment banking is part of the banking industry, and its primary business is sourcing and managing capital on behalf of other companies and businesses. Investment bankers are actively engaged in the planning and launch of Initial Public Offerings (IPOs), Mergers and Acquisitions (M&A) and various other big-ticket deals in the business and corporate world.
For investment bankers, there is a lot at stake. In the first nine months of the current year, global mergers and acquisitions have topped $4.5 trillion from over 40,000 deals. All these deals and exchange of money were made possible by relentless effort and data-crunching by investment bankers.
In the banking and finance sectors, data is essential to the business. But when you consider investment banking, data is all the more critical. It is what makes the job of decision-makers in this industry all the more complex.
For example, if a company is seeking an M&A deal, it will appoint an investment banker whose responsibilities will include creating appropriate financial reports, seeking eligible partners, preparing blueprints, investment ideas and the financial details of the deal. They have to analyse huge amounts of data and draw insight from it.
This brings data science and other tools of Artificial Intelligence into the picture.
Are Investment Bankers Ready for Data Science and Digital Tools?
Investment bankers can use Artificial Intelligence to work smartly. AI is a combination of data science techniques, machine learning advantages and data analytics insights. But for them to use Artificial Intelligence, there can be a steep learning curve to overcome. For example, anyone who wants to make sense of big data must have a background in computers, mathematics or statistics. Some knowledge of computer programming is one of the desired skills, if not downright mandatory.
However, once investment bankers know their way around data science and digital tools, they will witness a boost in their productivity and efficiency.
How Data Science Is Working Smart Even for Investment Bankers
Investment banking is one of those sectors that consume and generate a great deal of data. Data science tools and technologies are, therefore, crucial for the smooth functioning of this sector.
For example, data science involves creating appropriate solutions to analyse and draw meaningful inferences from unstructured data. One of the objectives of data science is to create structured data that can be used for business analytics. And, as we know, analytics is an extremely useful set of data insights that help decision-makers fine-tune their strategies and offerings.
The entire process of machine learning, data science and data analytics is equally important to investment bankers who have to delve deep into a sea of data and come up with a handful of pearls that we see as the details of the final deal. Machine learning tries to mimic and automate human intelligence. To do so, it uses available data and the data that it generates in the course of its normal functioning.
If investment bankers use AI tools, they can cut down significantly on manual and repetitive tasks, including data analysis that consumes much of their time, energy and effort.
Examples of Current AI Applications in Investment Banking
AI applications save costs for the global banking industry to the tune of $522 billion annually. They are deployed to generate leads and revenues, offer regular products and services and prevent fraud and theft.
Investment banking, too, has started using AI solutions to optimize its processes.
Katana is a predictive analytics tool from ING Financial Markets Global Credit Trading, London. It uses historical and real-time trade data to provide statistical forecasts. Pilot tests show that it offers 90% faster price quotations. It reduces the cost of trading, and traders can quote their best prices more frequently.
Kortical is a London-based company that offers predictive analytics through their AI platform, The Kore. It allows users to upload data directly from an Excel sheet and add “parameters” to create a machine learning model required to clean and analyse their data.
Investment Banking and Metadata Management
Investment bankers need to work much like data scientists to create clear, engaging and compelling business and financial models and marketing pitches. If you look closely, you will realise that a data scientist can perform the role of an investment banker with little to no orientation. Conversely, an investment banker requires relevant coding skills that can be used for data analysis and data analytics.
For example, like all bankers, investment bankers have huge amounts of data that need to be transported from one platform to another within and outside the system. This consumes a great deal of time and effort. In this context, metadata management that administers and describes other data assumes significance, and an investment banker needs to be conversant with it.
Metadata management creates processes and policies to ensure that available information can be easily accessed, analysed, shared and integrated. It helps to find, use, preserve and reuse data. An investment banker without any knowledge of metadata management may not be able to access and use data that may be available to him within a few clicks.
Investment banking is a global, high-value, and highly competitive area of banking. Like all other sectors, it depends heavily on data analysis and data analytics, not merely for the competitive edge but also for routine functions.
However, with the use of AI for solutions, investment bankers can cut down on repetitive and manual tasks and use their time and energy in high-value endeavours.
AI applications in investment banking are pivoted to three main pillars of Artificial Intelligence — data science, machine learning and data analytics. Investment bankers with some skills in these areas can use AI to their advantage.