How has AI impacted investment banking? Well, JPMorgan Chase has made it clear that they are now an “AI-first organization," and not a banking firm. This is how important AI has become in investment banking, that firms are now focusing on the “AI” aspect more than the rest.

Investment banks have always been ahead of the curve for the longest time. Anything that can make them money, rest assured that they will be there. Banks were the first to adopt and start using AI as it was a much smoother and safer tool to handle such extreme amounts of sensitive data.

Whether we are talking about major international banks, or regional ones, all banks are now widely using AI in investment banking to increase their safety and efficiency.

Background

It makes the most sense for banks to fully incorporate AI into their systems. After all, they are the most sensitive sector as they are dealing with large amounts of data that are just not possible to be computed by the average human brain anymore. The data keeps growing everyday as banks become more and more central to our lives.

One of the busiest divisions related to banking is investment banking. Investment banking deals with a lot of research, calculations, investments, and risk management on a daily basis. It has become a pillar in the economical world we live in today. 

As it is often said: in Wall Street, money never sleeps. This is where AI comes in regards to investment banking. According to the McKinsey Global Institute, just using generative AI could add between $200 billion and $340 billion in value across all banking.

There are some banks out there that are already fully incorporating AI into their systems, specifically Corporate and investment banks. These are the players that have been ahead of the game decades go. Trading teams have been using machine learning algorithms for a long time to predict trading patterns, way before the rest of the game caught on. They’ve also used natural-language processing to easily structure and read tens of thousands of pages of unstructured data. Years later, these banks are enjoying the benefits of AI, however, some of their tactics have become outdated. There are also still an array of jobs and tasks that are conducted using human capital, this is where generative AI comes in.

Generative AI

Recent studies have shown that generative AI can boost productivity in investment banking massively. Stanford researchers found that generative AI boosted customer service productivity by 14%. McKinsey have also reported that generative AI could improve their productivity in investment banking by 30-90%. They have also reported a possible 15% increase in operating profits using a generative AI tool. 

Deloitte is also reporting that using generative AI in investment banking could boost their front-office productivity by up to 35%. Not only that, but they predict that it will increase their revenue by $3.5 million PER front-office employee by 2026.

Before we get into the specific AI use cases in investment banking, it is important to note that generative AI is just one broader use. Its benefits can help traders quickly and more efficiently analyze and summarize company and industry fundamentals, run valuation models, conduct back-test trading strategies, and offer personalized trading recommendations to clients.

It can also be used to accelerate software delivery using assistants. This can help with code translation and bug detection and repair, as well as documenting results. These are just a few examples of how generative AI can be used in investment banking in a general sense.

AI Use Cases in Investment Banking

Risk Management

One of the most important uses of AI in Finance, especially investment banking, is risk management. AI is used to identify and reduce potential risks, as well as detect patterns. It can provide timely insights to make quick decisions, sometimes in real time, by scanning large amounts of data in a fraction of the time it would take a human to do the same thing.

Financial firms utilize AI to mitigate risks that are catastrophic for the industry. An example of that is credit risk. When doing any type of investment, AI will analyze how likely the borrower will face issues with paying back a loan. This borrower can be an individual, or an entire company. AI analyzes different types of data, such as credit reports, bank records, market trends, and other things. 

There are other types of risks, such as market risk. Examples of market risks include foreign currency, interest rates, commodity prices, and stock prices. A type of risk that AI completely wipes out is operational risk, which is basically any financial loss that happens as a result of internal errors or employee mistakes.

Fraud Detection

AI can help detect and prevent fraud by continuously monitoring transactions. It studies patterns and tendencies of a user to learn their spending habits, and if it detects an anomaly or suspicious activity that is out of the ordinary, it automatically raises flags and can halt any transactions instantly, even if they were approved by a human.

Fraud detection is one of the most important uses of AI today, especially when it comes to investment banking. AI and machine learning help banks find scams, reduce risks, find holes in their systems, and make online finance more secure.

By using AI, banks can easily identify fraudulent activities such as money laundering or other illegal transactions. These threats can sometimes miss the human eye and go unnoticed. But this isn’t the human eye we’re talking about, this is AI.

For example, HSBC has fully adopted an AI approach to combat money laundering schemes. They are using a cloud-based transaction monitoring system, with Google Cloud’s AML AI as its central component.

AI in investment banking also allows for constant cyber-attack surveillance, allowing banks to respond quickly to upcoming intrusions before they impact their customers, workers, or infrastructure. Malware detection by machine is now possible through supervised machine learning. 

AI in investment banking also offers constant enhanced defense against cyber attacks. Banks are not able to respond quickly in real time and stop potential hacks before they impact a customer or their own systems. Machine learning allows for that to happen by constantly updating defense tactics to adapt to the ever-growing tactics of cyber attackers. Machine learning algorithms allow AI to monitor networks, detect malicious software, and prevent data breaches.

A bank’s capacity to detect fraud grew by 50%, while the number of false positives dropped by 60% due to machine learning.

Portfolio Management

Machine learning and NLP are used by artificial intelligence to analyze data and then offer helpful portfolio management recommendations. AI can determine what assets to buy and which ones to stay away from. It assesses which assets to hold long-term and which ones to get rid of. These choices are also suited for the goals and specific interests of the investor or the client.

With AI at the head of forecasting, it is always a sunny day. It is almost guaranteed that every investment is profitable. AI forecasting accounts for so many factors simultaneously that a human analyst can never compute on his own.

Customer Service

Most people don’t realize this, but most customer service today is run by AI. Chatbots are run and managed by AI to help clients in banking by quickly answering their queries and helping their needs without the need to wait in a line or wait for a human agent to assist them. Chatbots offer a personalized guide that gives ideas and even carries the conversation, sometimes giving you insight on a problem you did not know. The more the chatbot learns, the better it gets at solving a client’s problem.

With chatbots running customer service, financial firms can cut down on legal tasks, cut down customer service staff, and have a much more efficient and accessible customer service experience.

Big Banks

The biggest banks around the world are using AI, but each one has adopted AI to suit their own specific needs. Each bank uses it differently, according to what they want and what they built.

Morgan Stanley, for example, has built a chatbot in collaboration with OpenAI’s ChatGPT, to assist its financial advisors. This chatbot provides analysts with instant access to their vast database that contains around 100 thousand research reports and documents, all within the click of a button. In mere seconds, any employee can access whether they want within this database.

In fact, Morgan Stanley reported that through the first half of this year, 98% of financial advisor teams have adopted an AI assistant that they developed for the company. This assistant gives company investment advisors fast access to Morgan Stanley’s entire inventory of investment-based intellectual capital.

Deutsche Bank announced a partnership with NVIDIA in 2022 to help them with their deployment of AI and machine learning for risk management. Wells Fargo, similarly, has partnered with Google Cloud AI to create the bank’s virtual assistant app, which is capable of doing more than 100 million interactions per year.

Benefits

With AI handling things in investment banking, firms can now enjoy a wide array of benefits, but it is not just limited to the CEOs and CTOs. Junior employees benefit from AI within the industry as well because of how much different their job becomes with the existence of AI. There is much less manual work for analysts, as repetitive tasks such as documentation or gathering data is now done through AI. Analysts can now spend more time actually analyzing, instead of just punching in numbers to collect data. With grunt work taken care of, analysts can now focus on quality tasks that help grow business and actually boost numbers.

Speaking of boosting numbers, stock trading is now more efficient through the use of NLP. AI analyzes historical trends and understands market conditions to be able to provide trade insights. It does that while “speaking the lingo” so to speak and use the exact terminologies that a trader would use. With AI on the steering wheel, it is able to give much faster and more accurate trade recommendations, thus limiting the chances of missing out on a rising stock before the trend has happened.

With AI becoming a norm, this has completely changed how some positions are viewed. Normally, an intern position, especially in an investment firm, was known for doing basically all of the grunt work. As we’ve mentioned that grunt work is now taken care of, entry-level employees can actually now be properly trained from the beginning to be built on AI, rather than just learn to adopt it.

Would you rather teach a dog a new trick? Even though it is able to learn something new, it is much more effective to just teach a young puppy everything that you need so that it becomes part of its nature. This also transforms the skill sets required for a new hire, completely revolutionizing standard positions at investment firms.

Challenges

Although AI has completely transformed the way investment banking operates, firms will still need to be careful of the risks that come with it. Not everything is perfect, and there are some issues that firms may need to address.

Generative AI can be biased depending on the data that it is given. Sometimes data can be imperfect and the engineering can affect the decision making process of the AI. It can lean towards certain decisions regardless of outcome depending on the “training” it received if you will. Just like any human being can be biased, the AI can as well.

There are also privacy concerns, which is usually the most concerning aspect when it comes to AI, especially in banking. AI may accidentally use sensitive client information and it can share it with a third party. AI will also have access to that information which a client may not necessarily have consented to. Security threats naturally appear when dealing with third-parties, and leaks could happen. This is the biggest risk especially when it comes to AI use in investment banking.

Conclusion

Investment banks around the world are developing a clear strategy for AI adoption. It is very clear that the future, as well as the present, is AI. Everyone is gathering resources to be able to fully adopt AI into their teams.

There is so much being invested in tech talents over the past few years. In fact, JPMorgan has more than quadrupled the amount of AI hirings and employees in the past decade. It is a sign that times have changed, and the whole world must now embrace this change.

“Over time, we anticipate that our use of AI has the potential to augment virtually every job.” Jamie Dimon, JPMorgan Chase CEO.