AI and the Future of Investment Banking

In a world that is becoming increasingly digital, automation has become an important part of our lives. From the moment we wake up to the time we go to bed, various processes are automated. Banking is no exception. The use of artificial intelligence (AI) is on the rise in investment banking.

AI is being used to automate and augment processes traditionally carried out by highly-skilled human professionals, including trading, analysis, and research. For example, JPMorgan employs an AI trading program that uses machine learning applied to billions of historical trades in order to outperform human traders and less sophisticated program trading applications. Recent research suggests that reinforcement learning techniques may even be able to optimize for the behavior of other market participants responding to automated trading strategies.

Other banks are using AI to analyze large data sets to find patterns that may indicate fraudulent activity or flag possible compliance risks. Private banking has been at the vanguard of AI adoption, with a recent industry study finding 68% adoption of AI decision-support tools in wealth management organizations. Overall, it is clear that technology is playing a larger role in the financial sector and that AI is playing an essential role in this area.

This article will discuss the future of investment banking and how AI will play a role in it. It is predicted that AI will take over many of the more routine tasks currently done by junior investment bankers while giving senior bankers greater leverage. Firms will need to evolve and elevate the junior banker role to stay competitive.

Opportunities and Challenges

Opportunities for AI in the investment banking industry are vast and largely untapped. As the AI/ML leader at a global top 10 investment bank remarked to us recently, investment banks have enormous volumes of data but are just starting to make use of it. With the Internet making financial markets data broadly available to everyone, banks have lost their information advantage and must climb the value chain by investing in more sophisticated analytics and AI.

According to Building the AI Bank of the Future by McKinsey, "Across more than 25 use cases, AI technologies can help boost revenues through increased personalization of services to customers (and employees); lower costs through efficiencies generated by higher automation, reduced errors rates, and better resource utilization; and uncover new and previously unrealized opportunities based on an improved ability to process and generate insights from vast troves of data."

However, several challenges must be overcome. First, banks must invest in the necessary infrastructure and tools. A senior digital investment banking executive pointed out to us that even the largest banks can no longer develop all of this technology internally and must partner work with emerging fintech. Second, data security and privacy issues must be addressed. Third, problems of algorithmic bias are widely recognized. Fourth, and more subtly, AI and ML systems are difficult to monitor and maintain. Researchers at Google have called machine learning "The High-Interest Credit Card of Technical Debt."

The Impact of AI on the Workforce

With AI being able to do things such as analyze data and recognize patterns, it will inevitably replace many of the more routine tasks currently done by humans. For example, if AI can optimize and rebalance portfolios or monitor and trade on changes in market sentiment, this could reduce the need for some technically sophisticated but essentially formulaic tasks.

According to a recent article from Deal Capital Partners, "With intense pressure coming from management to slash costs and maximize returns in the near-term, the movement of AI into investment banking will likely be hastened. In recent years, investment banks have moved jobs associated with compiling and checking data on customers and transactions offshore to lower-cost countries. When AI becomes mainstream, at least among banks, those jobs would be automated. It is expected that 4,000 investment banking jobs will disappear by 2025. However, we would expect to see an increase in technology-related jobs such as data analytics and programming."

Of course, the most valuable and differentiated investment banking skills are creative, strategic, and relationship-oriented. AI cannot build trust with a client or advise on an acquisition or an IPO. Yet AI can certainly provide analytics and insights to help bankers seize opportunities faster and smarter. AI will increase the efficiency of junior bankers, helping banks attract and retain top talent. Banks that augment their bankers effectively with such AI tools will take market share and grow at the expense of less innovative players.

"Bots are the New Apps"

One sign of the AI winter giving way to a bountiful AI spring was Satya Nadella, Microsoft CEO, remarking in 2016 that "Bots are the New Apps." Conversational AI bots, also known as chatbots, are certainly making their presence felts in the world of investment banking.

Morgan Stanley has a bot called AskResearch that helps analysts find the insights they need in an ocean of research reports that grows by 50,000 or so items every year.

Bots can also give bankers AI superpowers with hyper-personalized, proactive insights that are automatically tuned by ML to each banker's coverage areas and transactions. For example, ModuleQ's People-Facing AI can surface relevant research reports and timely market intelligence such as M&A transaction alerts from data providers such as Refinitiv/London Stock Exchange Group. To streamline workflow, ModuleQ's bot delivers these insights where bankers work in collaboration apps such as Microsoft Teams.

Many banks deploy chatbots for customer service applications, because they can handle a large volume of inquiries simultaneously and are available 24/7. Goldman Sachs uses chatbots to provide customer support for its online banking platform, Marcus by Goldman Sachs. JPMorgan Chase has developed a virtual assistant named "Julia" that helps customers with account inquiries, product information, and other banking needs. Julia is responds to over 60% of customer questions within 5 minutes.

Other banks using virtual assistants to improve customer service include Bank of America ("Erica"), HSBC ("Amanda"), and Citi ("Ava").

Research and Analysis

Banks have been utilizing artificial intelligence for years to research and analyze investments. However, with the ever-growing advancements in AI technology, banks are starting to explore new and more sophisticated ways to use AI to improve their investment processes.

One way that banks are using AI is in the area of predictive analytics. Banks are using AI algorithms to predict how certain investments will perform in the future. This helps bankers make more informed decisions when it comes to investing money.

Several different AI algorithms can be used for predictive analytics. The most common ones are decision trees, support vector machines, and neural networks. Each of these algorithms has its strengths and weaknesses, and banks will need to decide which one is best suited for their specific needs.

Using AI for predictive analytics can help banks make more informed decisions about where to invest their money. Banks can use historical data to identify patterns that may indicate how a particular investment will perform in the future. This information can help make decisions about whether or not to invest in a specific security or asset.

Banks are also using AI in the area of extensive data analysis. Banks are using AI algorithms to analyze large amounts of data to find patterns and correlations that would otherwise be undetectable. This helps bankers make better investment choices based on a larger data pool.

Additionally, AI can help banks speed up investment research by automating certain tasks. Banks can focus on higher-value activities, such as developing client relationships and assessing risk.

The Human Side of AI in Investment Banking

While its capabilities are impressive, AI has long been criticized for its cold, emotionless approach to decision-making. The investment banking industry has led to concerns that AI may eventually replace human managers and analysts altogether.

Bankers rely on AI to research companies and find new opportunities. However, AI should not be relied on too heavily. It is important to remember that humans are still involved in decision-making. Bankers need to use their judgment and experience when making decisions.

AI can automate specific processes and tasks, but it is ultimately a matter of human judgment to decide where to invest capital, especially in the context of major transactions that shape the future of businesses and industries. 

Conclusion

AI is changing the landscape of investment banking. AI will have a profound impact on the future of investment banking. Its ability to process large amounts of data quickly will help banks make more accurate and timely decisions, while its ability to automate routine tasks will free up bankers to focus on more complex work. Banks that embrace AI will be better positioned to compete in the future, and investors should consider this when choosing a bank to do business with. Bankers need to understand how AI works and how it can be used to improve their work.