The global Artificial Intelligence market size is valued at USD 26.23 billion in 2024, according to Precedence Research, and it’s fair to say that investment banking plays a major role in this growth. While it’s hard to overestimate the business impact of big data combined with powerful AI/ML solutions for investment banks, there are more and more technological breakthroughs and market trends emerging each day.In this article, we will share our insight on how exactly leveraging AI can help to improve various aspects of this sub-industry, enhancing efficiency and automating repetitive tasks that hold back the progress of organizations across the globe.

The Applications of AI in Investment Banking

It looks like the Investment Banking industry will never be the same with AI solutions already covering the essential parts of this domain, helping banks to deliver highly personalized, secure, and sophisticated services. Below are some of the most prominent applications of AI in investment banking. 

Applications of AI in Investment Banking

Risk Management

With the help of advanced AI models, it is possible to unleash the capabilities of predictive analytics and based on historical data analysis forecast risks that might occur. For investment banks, it means having the ability to anticipate:

  • Market fluctuations
  • Credit defaults
  • Operational risks. 

To top that off, modern AI systems offer proactive risk management, achieved by continuous monitoring of transactions and changing market conditions. 

Customer Service Automation

Machine Learning helps to personalize client interactions for investment banking and significantly boosts customer experience across various touchpoints. Robotic Process Automation truly shines here, as chatbots work great for increasing worker productivity due to managing basic inquiries, and guiding customers by sharing information. With advanced virtual assistants, it is possible to provide more sophisticated services, like highly personalized financial advice based on individual data. 

Serhii Leleko: ML & AI Engineer at SPD Technology

Serhii Leleko

ML & AI Engineer at SPD Technology

“Our use cases have proven that combining AI chatbots and virtual assistants with human experts is the most efficient way to leverage the technology. While chatbots are highly effective with the automation of mundane tasks, working in the assisting role is bringing the best of both worlds of innovation and human intelligence to result in superb customer experience.”

Portfolio Management

Numerous success stories have proven that AI-driven Portfolio Management delivers higher precision and deeper customization, compared to traditional approaches, eliminating the chances of human error. The most important aspects we should mention are: 

  • Robo-Advisors: AI systems that create and automatically manage investment portfolios customized to individual client goals and risk tolerances.
  • Optimization Algorithms: With the help of AI, it is possible to optimize asset allocation and rebalance strategies to ensure the highest ROI.
  • Personalized Strategies: Machine Learning algorithms are capable of offering personalized investment strategies based on rigorous analysis of huge amounts of data. 

In one of our projects, we developed an MVP for the Diligence Fund Distribution Platform, providing our Artificial Intelligence and Machine Learning expertise to introduce a highly efficient matching process for users of the product, as well as smart search functionality. This portfolio management solution allowed our client to pitch to investors and raise funds for the next iterations of this startup and evolve the project further.

Market Research and Analysis

With AI, the changes in investment banking market trends are easy to detect and follow, with deeper insights for a more data-driven decision-making process. It takes powerful sentiment analysis tools to analyze news and social media to gauge market sentiment and determine potential risks and movements. With modern neural networks and deep learning models, it is possible to significantly enhance data mining processes, having major advantages in the:

  • Feature Extraction
  • Pattern Recognition
  • Anomaly Detection
  • Classification and Clustering
  • Handling Unstructured Data. 

Learn about the groundbreaking potential of Machine Learning in Finance in our detailed article based on hands-on experience.

Generative AI for Investment Banking – Promising Opportunities

Generative Artificial Intelligence is set to become a transformative force in the world of investment banking, as the total potential added value of Generative AI could range between 200 and 340 billion U.S. dollars in the next few years, according to Statista. Let’s break down what solutions will stimulate this growth the most. 

The Opportunities of GenAI in Investment Banking

Improved Customer Onboarding

Generative AI can make the customer onboarding process significantly faster and more personalized. For example, it is possible to create tailored legal documents needed for onboarding, including contracts and compliance paperwork. AI can automate the Know Your Customer (KYC) process as well, by generating and verifying the necessary documents, cross-referencing data with multiple databases, and ensuring regulatory compliance. 

Financial Scenario Simulation

The creation of different hypothetical market scenarios is possible with Generative AI. Banks need to be able to generate those scenarios to make a reasonable evaluation of the potential risks and their impact on investment portfolios. With the capability to simulate market conditions and the consequences of implementing specific investment strategies, AI can suggest optimal portfolio adjustments, or at least provide ideas for optimization by human employees. 

Intelligent Virtual Assistants

The market for Intelligent Virtual Assistants is already booming across all industries, being valued at USD 14.77 billion in 2024, according to Mordor Intelligence. As for investment banking, these software solutions will allow advisors to focus on more important tasks, taking over routine and organizational activities like scheduling meetings with clients and sending reminders on key operations. 

In one of our projects, we built a high-load support AI chatbot that processes 99% of queries under 10 seconds. The developed chatbot completely transformed customer service for our client, as it performs flawlessly even during peak hours, showing the true value of AI for businesses.

Automated Report Generation

When there is access to quality data, Generative AI can handle creating comprehensive financial reports, including balance sheets, income statements, and cash flow analyses. Even more, detailed performance reports and research reports for investment portfolios can be easily generated, with AI highlighting key attention points and displaying the results in a visually appealing format. 

Market Event-Driven Insights 

Generative AI can provide real-time insights into market events and their potential impact on investments, effectively helping banks and clients stay ahead of the curve. With finely tuned ML models, it is possible to evaluate the potential impact of geopolitical developments, economic indicators, or corporate announcements, providing timely insights for clients and advisors.

As a real-world example of this, we can mention an ML-enabled News Trend Detection Service that we built for one of our major, strategic accounts, a US-headquartered global SaaS provider of data, research, and technology. This micro-service-enabled solution allows the efficient processing of around 50,000 articles per day or around 18 million per year to detect both promising investment trends and investment-worthy business entities within them. 

Implementing the solution has reduced the workload related to manual data processing by 50%, which has improved the precision and speed of news analysis by human experts. Combined with another ML-based service that helps researchers contextualize news items faster, our News Trend Detection Service has dramatically increased the overall efficiency of new analysis.

Machine Learning in Banking has become a true game changer for the industry, offering never-before-seen levels of automation and outstanding client service.

Discover more benefits in our article!

5 Undeniable Advantages of AI Adoption in Investment Banking

While there are some potential risks and concerns like ensuring data security in Fintech apps that should be addressed, the benefits of AI in investment banking outweigh any possible challenges. 

Advantages of AI Adoption in Investment Banking

Automation-Driven Cost Savings

A lot of mundane and time-consuming complex tasks will no longer be a problem since AI introduces a massive potential for automation. Not discarding the capabilities of the human brain, AI can be much more effective for tasks like transaction processing or compliance checks, saving time for organizations on each operation. These efficiency gains translate into improving overall productivity and positively affecting the bottom line. 

Data-Informed Decisions That Lower Investment Risks

Even the best experts in the industry can miss some of the patterns hidden in the market data. AI algorithms, on the other hand, can derive game-changing insights based on automated data analysis. Organizations can benefit from this and make informed decisions on asset allocation, risk management, and portfolio optimization.

Better Customer Engagement Through Personalization and Support

It is possible to implement AI into your Customer Relationship Management (CRM) system and deliver the functionality of tailored financial advice to your customers. Leveraging the advancements of Natural Language Processing for business, AI can convert human speech into text and vice versa, enabling clear and quick communication. Based on these interactions and analyzing the answers and the sentiment, investment banking institutions can anticipate customer needs and offer relevant products proactively. 

There are also chatbots and virtual assistants, that with the help of AI can:

  • Offer 24/7 customer support
  • Rapidly answer questions
  • Deliver account status updates
  • Guide through key banking processes. 

More Profitable and Data-Supported Trading Strategies 

There is always room for improvement in trading strategies. AI in investment banking can bring in massive revenue streams by identifying opportunities in capital markets. Keep in mind that AI models are constantly learning, and they’re not only good with their initial strategies but also improve on them based on new data, becoming more efficient over time.

Proactive Cyber Threats Identification and Prevention

AI systems can monitor network traffic, analyze patterns, and detect anomalies that may indicate potential security breaches, which is critical for the industry. On our blog, we discussed closely AI-driven credit card Fraud Detection and investment banking can benefit from mitigating potential risks just as much. The clients of banks that fully leverage the capabilities of AI for a proactive approach to cybersecurity are maintaining client trust and always ensuring adherence to regulatory compliance to the highest extent.

How Investment Banks Can Prepare for an AI-Driven Future

You may not have in-house AI talent on your payroll yet, however, there are ways to get ready for a gradual adoption of Artificial Intelligence and set your organization on the path of technological improvement. Here is a step-by-step action plan on how your investment banking institution can get ready for AI transformation. 

5 Steps to AI Transformation in Investment Baking

Establish a Strategic Plan for AI Adoption

It makes sense to start with a comprehensive plan for implementing technology in your organization. Focus on having clear, tangible, and achievable objectives for the AI in your organization. In case you don’t have the necessary perspective, feel free to contact an experienced AI solutions development vendor to enable informed decision-making for the integration process.

Your strategic plan should include:

  • A detailed roadmap of the integration steps
  • Detailed budget and timelines
  • Key performance indicators. 

Construct a Contemporary Data Infrastructure

You should invest in scalable and flexible data storage solutions that can handle vast amounts of data from multiple sources, to have a strong basis for an AI-powered solution. This includes building a centralized data repository where data can be collected, stored, and processed efficiently. Make sure to leverage the capabilities of cloud computing technologies and establish clear data pipelines to facilitate seamless data flow. 

Identify and Mitigate Potential Risks

Of course, there are risks, and the most dangerous are related to data privacy since investment banking is working with sensitive financial information. That’s why it is critical to:

  • Ensure compliance with all relevant data protection regulations.
  • Implement robust cybersecurity measures to prevent any breaches during or after data entry.
  • Conduct regular audits of AI systems for bias.

Seamlessly Integrate AI with Current Systems

While the transformative power of AI in investment banking is undeniable, the proper integration process is key to securing the cost efficiency of the innovation. Close cooperation between IT, operations, and business departments is mandatory to implement new AI tools and not disrupt any critical processes. We, at SPD Technology, know it first-hand, and ensure smooth integration with the help of:

  • Gradual upgrade of legacy systems
  • Adoption of middleware solutions
  • Leveraging APIs. 

Be Informed on Fintech Trends to Stay Competitive

Nothing ever stays the same, and this is especially true for AI in investment banking. The next big thing in Fintech can turn everything upside down in moments, so it is super important to keep your finger on the pulse of the latest innovation. Participating in industry conferences, engaging with Fintech startups, and collaborating with academic institutions can help you in this area and secure the market positions of your organization. 

Achieve a Competitive Advantage in Investment Banking with SPD Technology

At SPD Technology, we have been building top-tier software solutions for nearly two decades already. The fintech software development services we deliver to our global customers strategically merge the deep understanding of the banking business domain with the next competencies in emerging technologies such as: 

  • Artificial Intelligence and Machine Learning
  • Data Engineering and Management
  • Cloud Development and DevOps
  • Cybersecurity and Resilience
  • Payment Software Engineering. 

Our company excels in delivering future-proof cutting-edge solutions that secure leading market positions for clients, earning our rightful place among top Fintech development companies.  We are versatile, holding extensive experience in multiple domains, with a heavy emphasis on the Financial, Legal, Insurance, and eCommerce industries.

One of our tenured clients is Morningstar, a premier U.S. asset management company that employs more than 12,000 people, operates in 32 countries, and manages an investment portfolio of over $200 billion. During our fruitful partnership, we delivered multiple outstanding technological solutions that move the evolution of the client company forward.

When we overhauled and expanded the data collection application, our team helped the client with implementing complex business logic that consisted of around 150 business rules. The new system that we introduced opened a new business line for a client and offered 10x more processing power and stability compared to the previously developed MVP. 

In another subproject, we shifted our focus to transforming a legacy investment analytics app with serverless architecture. Our experts changed a slowing-down legacy system with a robust and investor-centric platform that can withstand 1 million users daily. The strategic decision to transform a legacy system and migrate it to the cloud resulted in a significant reduction of operating costs for customers, as well as improving the availability of services and broadening the customer base.

The streak of successful solutions didn’t end there, as we also offered significant benefits to the client with an AI-enabled Web Crawler tool that automated core business lines. Thanks to our proficiency in Artificial Intelligence and Machine Learning, we reduced time for data analysis from 2 to 3 days of manual work to 2 to 3 hours of AI model performance. As an additional bonus of this solution, our client received 10x data storage cost reduction thanks to optimized architecture.

Conclusion

The wider adoption of Artificial Intelligence in investment banking is already displaying greater efficiencies, surpassing the traditional ways of doing business. The research by Deloitte on productivity gains due to implementation of generative AI, states that by 2026 AI can boost productivity for front-office employees by as much as 27%–35%. And this is only one of the areas that can be enhanced, as AI-driven solutions are expected to boost seemingly every aspect of the Banking Industry. 

For investment bankers, fully embracing AI/ML is no longer an option, but rather an essential measure to remain competitive in a quickly changing environment. We, at SPD Technology, are ready to help you go beyond what was previously possible in investment banking, shattering the boundaries of traditional approaches with state-of-art technology and proven experience in delivering Intelligent Automation to leading organizations across the globe.

  1. Our custom AI solutions development services are already elevating the work of investment bankers to an entirely new level.

    Start driving digital transformation in investment banking without the disruption of any critical business processes.  

FAQ

  • How to Use AI in Investment Banking?

    Artificial Intelligence is already can be considered a major force in the Investment Banking Industry, with the most notable areas that can be transformed including: 

    • Algorithmic Trading: AI models can handle some essential, critical tasks like optimizing strategies and predicting market trends.  
    • Risk Management: based on historical financial data, AI-powered Fraud Detection systems can detect patterns and alert on suspicious activities. 
    • Customer Service: AI-powered chatbots and personalized financial advisors are already changing the industry, by offering massive cost reduction for organizations. 
    • Portfolio Management: robo-advisors based on AI technology not only manage and create investment portfolios for customers but also optimize portfolio allocation.
    • Due Diligence and Compliance: with the capabilities of modern solutions, it is possible to conduct quick due diligence by scanning large volumes of data.