In the field of investment banking, automating tedious manual processes is not a new phenomenon or attempt. For instance, junior bankers slogging over financial models have often turned to macros to simplify the process somewhat. This however far from eliminates the several hours required to be spent staring into a screen, as the time saved is not that significant. The process largely remains high on the usage of labor and time.
Can artificial intelligence simplify the work of investment bankers?
AI in investment banking is not as far-fetched a concept as it may seem. Back in October 2017, American investment banking firm Bank of New York Mellon Corporation – more commonly known as BNY Mellon – announced that across its portfolio, it was automating tasks and reducing the tedious manual effort that its employees needed to expend, by putting into place more than 250 robotic process automation (RPA) systems. Of these 250-odd processes, around 220 were developed by Blue Prism. BNY Mellon claimed that the RPA systems helped it to realize the following:
- An 88 percent improvement in processing time
- A 66 percent improvement in trading time
- Reconciliation is just a quarter of a second
Has AI gained ground among the wider set of investment banking professionals?
The potential offered by AI for investment banking is not easy to quantify. The advantages to gain are immense, though, especially in such a competitive market where organizations make every attempt possible to achieve a better customer experience, higher lead generation, and more profitability.
Some interesting findings from a survey conducted by Greenwich Associates in June 2018 are shared below:
- Only 17 percent of investment professionals employed artificial intelligence in their work processes
- About 40 percent of respondents to the survey shared that they planned to grow their budgets to deploy AI in the future
Plenty of jobs are also available in this domain. As per a February 2019 study conducted by efinancialcareers, there were 93 open job positions related to AI advertised for by the major US banks.
What are the top use cases for AI in investment banking?
AI and investment banking are far more closely related than might be imagined at first thought. The biggest benefit appears to be the saving of time for an analyst slogging away on tedious manual processes. There are, however, many other advantages of deploying machine learning in investment banking for the banks themselves as well as for their junior and senior employees.
Here are some of the chief use cases:
- Collecting market data: Investment bankers often find themselves drowning under an information overload. Associates are often on edge with the pressure of knowing the latest happenings as they occur and often ahead of the actual occurrence. By deploying AI in investment banking, analysts could receive significant assistance in the collection, analysis, and classification of the latest news and market sentiment alerts.
- Predictive analytics: AI comes to the picture by looking at historical data running through an algorithm to choose certain scenarios and predict the likely future outcomes. Representing a target-specific implementation of data science, it was seen in the work of French bank BNP Paribas. The bank used an AI tool it created, and identified patterns in data records of trades made through BNP Securities Services, looking for the likelihood of a particular trade leading to a failure and thus needing preventive manual intervention.
- Processing trades: Another use of AI for investment banking, is about the technology being deployed to automatically execute trades after identifying systemic investment strategies across multiple markets as needed. Routing algorithms match buy or sell orders from traders with exchanges, stockbrokers, and trading systems capable of meeting that order.
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