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AI in Investment Banking

Snowflake Snowday 2021

Investment banking is a data-centric industry. Financial institutions have long leveraged big data to enhance customer experiences, maximize profitability and manage risk. The introduction of AI in investment banking has spurred a fresh wave of data-driven innovation and growth. Sophisticated types of artificial intelligence, such as generative AI, enable corporate and investment banks to use their data in new ways, at scales not possible just a short time ago. In this article, we’ll explore five ways financial institutions are using AI in investment banking and share steps you can take to future-proof your AI strategy. 

High-impact use cases for AI in investment banking

Powerful AI algorithms transform how corporate and investment banks navigate complexities, enabling forward-looking organizations to enhance their competitive edge in a rapidly evolving industry. Here are some of the most high-impact use cases of AI in investment banking.

Client relationship management

Investment banks connect clients to the broader financial market, assisting them with a range of services, including initial public offerings (IPOs), corporate mergers, debt financing, and wealth and asset management services. Building and maintaining positive client relationships is important to any business, but it’s crucial for those that are built on trust, as investment banking is. AI can help relationship managers more effectively collect, organize and distill information gathered from across the business to generate marketing content, investment theses, due diligence reports, research reports and pitch books personalized to the needs of individual clients. 

Market sentiment analysis

AI can help investment banks to better understand current market dynamics and accurately anticipate and adjust to future trends. Natural language processing (NLP) and generative AI enable investment bankers to analyze data at scale, drawing information from a diversity of sources including mandatory filings, regulatory reports, media websites, and social media sources to infer sentiment. This data can be used to help clients refine and adjust their investment strategies in response to shifts in market sentiment.

Portfolio optimization 

Generative AI can also be used to create advanced simulations that game out a variety of potential market conditions, interest rate models and more. Using synthetic data and algorithmically generated artificial data, AI-powered simulations can be used to fine-tune trading strategies, improving portfolio performance while respecting predefined constraints such as risk tolerance and return expectations.

Risk management

AI can help investment banks and their clients more accurately forecast risk to better balance the overall level of risk they are willing to tolerate against the potential benefits and costs involved. Generative AI models can assess and forecast exposure to risk across a number of areas including interest rates, credit, liquidity, and potential for default, providing a clearer picture of what’s at stake.

Regulatory reporting

Government regulations require covered institutions to conduct stress testing to gauge their ability to absorb losses during periods of financial stress while maintaining their ability to lend and meet obligations to creditors. Generative AI models can simulate a diverse range of adverse market conditions, helping teams comply with stress test requirements. To create these simulations, these advanced models use a combination of synthetic data and real data from numerous sources such as historical events, current market conditions, and potential future risks. Artificial intelligence is also able to create draft versions of technical documents such as environmental, social, and governance (ESG) and audit reports, pulling required data from across the organization.

How investment banks can position themselves for success in an AI-enabled future

While the strategic use of AI in investment banking creates new opportunities, there are several factors to consider when implementing AI to maximize its potential and minimize vulnerabilities. 

Develop a clear strategy for AI adoption

To avoid overwhelm and lack of focus, executives must develop a comprehensive plan for integrating AI into the processes most likely to benefit from the technology. The creation and use of AI in investment banking applications can require significant resources, so it’s important to identify the areas of the business where its use is likely to generate the most value. Potential high-value target areas may include marketing, sales, decision support, research and trading. 

Build a modern data infrastructure

Effectively integrating artificial intelligence across an organization requires a modern data architecture—one with fast, elastic compute resources, near limitless data storage, secure data sharing, and the ability to work with all types of data, including unstructured data. As the diversity of available data increases, incorporating unstructured data such as text, images, and client documents is becoming vital. 

AI systems also require access to a single source of truth. When relevant data is siloed across disparate systems, AI systems may not have all the data they need to make highly accurate predictions. AI initiatives also benefit from a fully managed service that enables building and deploying AI models without having to copy data, so that governance is preserved. 

Consider and address risks

Introducing AI into investment banking processes can present risks that must be considered and mitigated. One example is the need to protect and secure potentially sensitive data that models use for training. New applications may introduce security vulnerabilities that leave data and systems open to compromise. 

Additionally, models trained using personally identifiable information (PII), client-sensitive data, or sensitive internal data must be adequately governed to prevent this data from being included in the content it generates and presents to data consumers. Issues with training data or engineering decisions can introduce unintended algorithmic biases that result in negative outcomes for some groups or individuals. 

Lastly, the use of AI may negatively impact public trust if the models being used are opaque in the way they arrive at decisions. Creating models with high explainability allows all stakeholders, including the public to easily understand how the AI models operate. 

Advance your AI initiatives with Snowflake

Snowflake provides the AI-ready data infrastructure investment banks require. With the Financial Services Data Cloud, financial institutions can unite their siloed data, easily discover and securely share governed data, execute diverse analytic workloads, and securely build and deploy powerful LLMs and ML models. With Snowpark ML, now in public preview, developers can quickly build features, train models and deploy them into production—all using familiar Python syntax and without having to move or copy data outside its governance boundary. Snowflake empowers investment banks to bridge the gap between AI and business value.