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The rise and proliferation of AI has impacted nearly every industry or sector across the world, and investment banking - finance’s favorite - is no exception. Artificial Intelligence (AI) is set to shake up the investment banking industry, transforming the way finance operates. With its powerful capabilities, AI can streamline processes, increase efficiency, and enhance decision-making like never before.
Let's explore the profound impact of AI on investment banking and delve into the exciting future of finance. By leveraging machine learning algorithms and data analytics, AI can analyze vast amounts of information in real-time, providing investment bankers with valuable insights and predictions. From optimizing portfolio management to executing complex trades, AI algorithms can make faster and more accurate decisions, reducing risks and increasing profitability.
Investment banking tends to be most well-known for its long hours and generous salaries. Consisting of divisions such as mergers and acquisitions (M&A), equity financing and debt financing, investment banks help clients either sell or raise capital, or restructure their companies entirely. Some banks also diversify into asset or wealth management and have trading divisions that know the ins and outs of stock markets to conduct financial transactions for clients (Source: Investopedia).
So how can this branch of banking expect to be impacted by AI? Although not exhaustive, here are 5 ways this may manifest soon.
With generative AI, junior bankers will see a drastic decrease in the amount of manual everyday tasks performed. From report generation to writing pitchbooks and term sheets, and building financial models, investment banking analysts work 100+ hours per week gathering and putting information together (Source: Business Insider). Generative AI would not only cut down some of these hours but also help channel more time towards analyzing data instead of simply collecting and consolidating it.
On the other hand, this could also mean less analyst jobs in the future, because there’s simply less grunt work to do. Yikes.
Stock trading could be made much more efficient using techniques like natural language processing (NLP) and sentiment analysis (Source: Deloitte Insights). Analyzing historical trends, understanding market conditions for optimal risk management, and balancing risk and return are all necessary prerequisites to executing the perfect trade, but this can be time taking and challenging.
With the ability of artificial intelligence to read and process large amounts of complex data, and make recommendations, we might have faster and better trading in the near future.
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The introduction of AI in the money markets and international dealmaking won’t be all sunshine and rainbows. As an investment banking professional writes in Forbes, what happens if AI algorithms are trained based on flawed market assumptions or misread historical trends? (Source: Forbes) And if that information is used to execute financial trades? This could result in huge financial losses across the board.
Ensuring the right safeguards are in place to regulate actions taken or recommended by artificial intelligence, and understanding the mechanisms driving it, will become paramount in the new environment.
While real human interaction will always remain essential in investment banking, considering the high stakes nature of the industry, AI could supplement the customer experience by making it more automated and personalized. AI chatbots trained on customer data can answer day-to-day queries and make recommendations regarding basic financial decisions (Source: Medium).
However, this is unlikely to replace investment banking professionals, considering the human elements of trust, relatability, and tangible relationship-building that the work often demands.
As AI replaces much of the manual, repetitive tasks that investment banking analysts have traditionally spent most of their time doing, the industry may have to rethink the skillset required of these entry-level team members. Analysts may find themselves more involved with the analysis of information to derive meaningful insights, rather than just information gathering. They may also spend more time thinking about how best to present a problem and its solution in front of a client, requiring them to have more of a strategic rather than a research and administrative skillset.
This will necessitate a change in how recruiters, mentors and senior team members hire and train investment banking analysts.
The impact of AI on investment banking is profound and many major players have already invested in artificial intelligence to accelerate growth.
In 2018, Morgan Stanley created an AI-driven solution “Next Best Action” for client communications. The system improves the relevance of customer communication by giving financial advisors tailored recommendations based on machine learning and advanced analytics. It crawls through large amounts of data such as market trends, economic indicators, and individual clients' investment histories and preferences. It then generates personalized recommendations, which are shared with the financial advisors. Advisors can proactively address client needs and identify potential opportunities or risks for the clients, enabling advisors to provide timely advice.
Goldman Sachs’ MARCUS is an online platform that leverages AI to provide personalized financial services to clients. It works by using machine learning algorithms to identify and mitigate risks proactively by suggesting strategies to the clients. For instance, the system might identify a client's overexposure to a particular sector based on market volatility or regulatory changes. It can then recommend portfolio diversification to mitigate this risk, allowing the client to proactively protect their investments.
(Source: LinkedIn)
The predictions surrounding AI applications in banking are ambitious: productivity of bankers is forecasted to rise by nearly 34% (Source: Deloitte Insights), with revenue per employee also increasing by a cool $3 million (Source: Business Insider). Although these are encouraging signs, especially considering the many benefits AI can bring in terms of automation, personalization, and efficiency, the application of AI in investment banking requires careful consideration and monitoring in order to avoid financial losses.
The transformation has already started. JP Morgan, a global premier investment bank, reports using AI to analyze loan agreements – saving 360,000 hours of manual work in the process (Source: IBCA).
As similar use cases emerge with more frequency, it will start to become clearer where AI’s strength really lies in investment banking, and how to leverage it for optimal, safe output.
By Manaal Shuja.
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