How AI in Investment Banking Boosts Efficiency

Digital banking began in the 1980s, ushering in technologies like automated teller machines (ATMs), and later advancing to online banking and mobile apps.¹ Artificial intelligence (AI) and its advanced applications are the next wave of innovation.

The banking industry has used some form of AI for many years, through tools like automation and machine learning, but more advanced applications are now emerging and seeing rapid adoption. The global banking industry is expected to invest roughly $632 billion in AI technology by 2028.²

What will that look like, and how does it impact investment banking? Keep reading to discover how AI in investment banking increases efficiency in customer experiences, risk management and security.

Enhancing Customer Experience

Customers want a seamless banking experience that keeps their information secure. AI is making that possible. The Organization for Economic Co-operation and Development (OECD) surveyed global finance industries and found that 49% of OECD-member countries are already using AI for customer relations.3 These include many customer-facing AI tools that enhance the experience:³

• AI chatbots 
• Virtual assistants
• Robo-advisors
• Digital onboarding with facial recognition

These tools give customers 24/7 access to customer support, where they can quickly ask questions and receive answers. Robo-advisors and targeted marketing tools personalize the experience, offering tailored financial advice and product recommendations that meet or solve customer needs.³

Additionally, AI-powered onboarding simplifies the process for both institutions and customers. Facial recognition and natural language processing (NLP), a type of machine learning, are used to identify customers and analyze and interpret data input.^3,4 With these technologies, the customer has a fluid and easy experience, increasing satisfaction.

Fraud Detection and Security

Advanced AI in investment banking is also transforming how professionals deal with fraud detection and security risks. For instance, machine learning in investment banking monitors transactions in real-time and automatically blocks fraudulent transactions or sends them to an agent for review.⁵ This model is trained on transaction history datasets and identifies patterns and items that don’t fit.⁵

Additionally, institutions can use behavioral biometrics solutions to identify anomalies. These tools use a type of AI called deep learning (multiple layers of neural networks) to analyze keystroke patterns and transaction timing to find irregularities. The models are trained on credit card detection and financial transactions datasets and can achieve about 98% accuracy and 96% precision rates.⁶

Automating Operations

Many back-office tasks are almost impossible to do quickly, given the volume of transactions and documents that banks encounter. AI, specifically robotic process automation (RPA), speeds up these tasks, which helps improve the customer experience and daily business management. For instance, banks may automate data collection and internal tasks, such as reconciling the treasury or forecasting cash flow. Other institutions may use optical character recognition (OCR) to analyze, interpret and organize or process documents.³

Investment banking automation tools may also run credit scoring and loan approvals, automatically analyzing credit information or flagging missing details.³ Generative AI tools auto-draft legal contracts or customer emails and communications, all of which speed up the approval process.⁷

Risk Management and Compliance

Predictive analytics is a forecasting method that uses historical data and statistical modeling to estimate future outcomes.⁸ These forecasting tools are essential in investment banking and provide insight into market and credit risk. Risk management departments might use predictive solutions to estimate stock prices and manage liquidity during market volatility.⁸

Anti-money laundering (AML) detection systems involve several forms of AI, which monitor transactions, identify irregularities and verify customer identities.⁹ Banks must report on these anomalies and suspicious activities. Automated solutions can collate this data, summarize it and generate a report for submission. AI solutions may even auto-file the reports, simplifying the process for internal staff exponentially.⁹

AI in Wealth Management

Banks and wealth management firms are optimizing AI and investment banking tools, such as robo-advisors. These solutions collect data from customers about goals, risk tolerance levels and income and assets to auto-create and manage a portfolio for them.¹⁰ These are low-cost options for customers and provide efficient portfolio management methods for investors.

Predictive models may also help firms optimize portfolios by identifying trading patterns, analyzing the risk-return of trades and forecasting outcomes.⁹

Challenges of AI Adoption

AI for investment banking is taking over some parts of the finance industry, but it's not without its challenges. AI-driven investment tools are projected to be the main source of advice for more retail or lay investors, growing 80% by 2028.¹¹ But will AI replace investment bankers? It’s unlikely.¹¹ AI governance is a widespread concern for consumers and regulatory bodies. Banks must be able to explain how AI tools make decisions; otherwise, they risk facing accusations of bias, such as in loan denials.⁸

Data quality is also a concern. If firms and banks use low-quality datasets, the risk of costly mistakes is high.⁸ Bad data could lead to poor predictions, leading to market volatility and financial losses. If multiple players use the same information to make similar decisions, it could have a negative snowball effect, leading to extreme market-wide volatility.⁸

Additionally, many institutions choose to use third-party vendors, which creates a weak link in the system and a possible cyber threat or third-party dependency.⁸ With a high volume of data collection and use, banks must have data privacy and cybersecurity controls in place.

AI technology is also expensive and difficult to integrate.⁸ Newer solutions may not work with existing internal systems, and private institutions may have more advanced technology than others in the industry. These gaps create operational challenges.

These answers lie in real human input and oversight. The industry needs skilled investors who can evaluate AI predictions and activities, as well as expert AI professionals who can manage AI solutions.

Creating Tech-Savvy Finance Professionals

Technology is transforming business and finance, but the fundamentals of numbers, data, and statistics remain as vital as ever. Leaders who are skilled in both quantitative analysis and technology are best equipped to help their industries adapt to change.

The Online Master of Science in Finance (MSF) program from the Raymond A. Mason School of Business prepares students to succeed in today’s complex financial environment. Students will develop quantitative and leadership skills to identify opportunities, assess risk, navigate global challenges, drive change and create value ethically. Students can graduate with both a master’s degree and a specialized finance certificate in as few as 16 months.

Schedule an appointment with an admissions outreach advisor to learn more about the Online MSF program.