4 AI Use Cases in Investment Banking
The capital markets continue to embrace technological innovation and how emerging technologies can improve business intelligence and create efficiencies. Research shows that the global AI fintech market will reach $46.9B by 2030. AI and ML are among the most revolutionary advances as they continue to transform the finance industry with their broad scope of potential uses and applications.
Investment banks have always been leading adopters of new technology, and AI and ML integration has been no exception. From better risk management to a more streamlined deal-sourcing process, AI is helping investment banks become more efficient with their resources on both the buy and sell-side.
Solving Challenges with AI in Investment Banking
The lack of access to relevant and quality data is a significant challenge investment banks face. Though we live in the age of information, data is not always readily accessible or organized in a way that is easy to analyze or extract insights from. Additionally, with the amount of data available today, it would be impossible for financial professionals to manually analyze and dissect it all, let alone provide meaningful and actionable insights.
While beating the competition to deals and acting quickly to make informed decisions are critical aspects of doing business in this field, it is often a lengthy and time-consuming process to qualify targets and perform due diligence.
Thus, implementing AI in investment banking is a big step these firms can take to become more efficient with their time and save on operational costs. Plus, AI can easily extract insights from structured and unstructured data, making limited data access a problem of the past.
Overall, AI enables investment banks to enhance productivity by providing these firms with around-the-clock data mining and deal sourcing capabilities. Utilizing AI and ML in investment banking offers precise and quality recommendations based on the mountains of data processed in a short amount of time. With this improvement, investment bankers can quickly meet the client requests by accessing the comprehensive data readily available through a query rather than manually sorting through documents and databases to find relevant information.
4 Use Cases of AI in Investment Banking
Various functions within investment banks can be improved or augmented when integrated with AI or ML, from the front to the back end. AI is growing in popularity due to its automation and data processing capabilities that save firms both time and money and its ability to help firms better deploy their resources and stay competitive in today's market.
1. Researching and Analyzing Market Data
To make informed investment decisions for the firm and its clients, investment banks must stay up-to-date on the market and new developments in individual companies or industries. With the extensive amount of data available and the constant flow of news and information today, it can become overwhelming for financial professionals to stay informed on all relevant news stories in real-time.
AI and ML never have downtime and can work at all hours of the day to scour the internet for new press releases, social media posts, interviews, quarterly filings, and more that may be relevant to a current or potential investment by the firm. Providing actionable and meaningful insights for analysts and investors, using AI in the investment banking industry can streamline the research process for these professionals and provide them with only the most relevant information.
Investment banks must act quickly on quality data and insights to jump on the right market opportunities and stay competitive. Having AI and ML automate back-office data processing and research tasks can help firms be more efficient with their staff and allow teams to focus on value-added tasks requiring human creativity and critical thinking.
2. Algorithmic Trading for Investment Banking
Another application for AI and ML in investment banking is trading. When done manually, executing a mass amount of trades can be tedious work, plus the trader must take action at the correct time to achieve the proper entry or exit point for an investment. For specific strategies, if the trade is done a second too soon or too late, it could cost the firm and its clients millions of dollars.
Using AI models to do algorithmic trading can enhance an investment bank's performance and help them act on opportunistic trades that human workers might not catch or be able to execute. Plus, the algorithms can accurately assess the risk exposure in real-time and act accordingly in the firm's best interest.
AI in investment banking can help firms make trades automatically across all regions or markets with speed and precision, ensuring that the trade is executed at the right price using the buy or sell orders set by the firm. Additionally, in the case of a failed trade, AI and ML algorithms can quickly determine the trade processing error and alter the system not to prevent it from happening again, which would be complex and time-consuming to investigate manually.
3. Anti-Money Laundering
A significant threat to investment banks is money laundering activities that the bank unknowingly facilitates. Exposing the firm to both legal and monetary risks, these banks must be able to identify possible suspicious behavior for further investigation. As organized crime and money laundering tactics become more advanced, investment banks need to ensure they are utilizing the latest technology to defend themselves and enhance their detection capabilities.
With AI and ML in investment banking, these firms can better understand who their clients are and can even detect potentially risky customers before they have the chance to act. Not to mention, there's an opportunity for significant cost savings here. Recently, a study by Finextra estimated that financial institutions spend nearly $214 billion each year fighting money laundering and other financial crimes.
While there's a growing push among consumers and regulators for increased visibility and transparency about who banks do business with, it's more important than ever that they bolster their anti-money laundering capabilities. AI can assist with this by monitoring information in real-time and providing real-time updates around suspicious transactions, and even preventing such activity from occurring in the first place.
4. Predictive Analytics
A wish among all financial services firms is to predict the future, and with the use of AI and ML in investment banking, that dream may not be so far-fetched. Through predictive analytics, these algorithms can take historical data and information to detect patterns and predict future scenarios and outcomes under certain conditions.
The implications of this capability are immense, with investment banks able to anticipate future stock market moves, client behavior, and emerging sectors ahead of the markets. These indicators for future events can drive the bank's competitive advantage and generate higher performance.
By utilizing AI in investment banking, firms can perform better decision-making based on the meaningful insights gained from predictive analytics. Timing is everything in the markets, so having the tools to provide insights into future events and opportunities can help firms become more efficient and accurate with their investments.
Integration of AI and ML in investment banking transforms how these firms do business, and investment banks continue to rank at the top for financial services firms adopting emerging technologies. Investment banking’s technology adoption is no surprise given the enhanced decision-making that can come from the insights and efficiencies provided by these models, allowing investment bankers to act quickly and stay competitive in today's rapidly-evolving environment.
How No-Code Simplifies the Use of AI in Investment Banking
For investment banking professionals who are looking to create value for clients, perform better risk management, or make consistent and successful deals, integrating AI into your operations can be groundbreaking for your firm. As the financial services field continues to be transformed by emerging technology, it will become increasingly important for investment banks to remain competitive and efficient with the help of AI.
While AI and machine learning models can be complex to build, you don't need to be an AI specialist or software engineer to implement them into your firm by using a no-code AI solution like Accern. To see how no-code AI could help your firm implement AI to stay competitive and make more informed investment decisions, request a free demo with Accern today.