Generative AI use cases for Asset Management Firms

Our generation is witness to the emerging power of current game-changer Generative AI (GenAI), which is a deep learning technique of Artificial Intelligence within Machine Language umbrella. It’s a thriving baby with Large Language Models (LLMs) as foundation, which itself is a convergence of neural network models, made commercially available now due to technology advances in transformers, encoder-decoder mechanisms using vectors.

GenAI is a very powerful tool due its ability to converse with users addressing their questions to produce outputs in text, code, image, video and other formats. To make the base model “production ready” in an organization context, LLMs need fine tuning with specific contextual information and human intervention to train the model.

GenAI for Financial Services

GenAI combines human intelligence with machine intelligence by understanding multi-turn conversational questions from business users to provide highly accurate answers. Financial services being a customer centric business, behavioral patterns play a major role in various decisions made by investors, financial advisors, fund managers and other market intermediaries. GenAI can assist individuals to analyze market news, economic indicators, research documents, current portfolio holdings according to their behavioral patterns. GenAI can interact with users to understand their requirement and create refined answers, formatted reports, charts including images.

GenAI for Asset Management firms

GenAI can provide valuable insights for various teams such as marketing managers, research analysts, product designers, traders, portfolio managers, risk analysts, etc. within AM firms to generate alpha with reduced risk through effective decision making.

Marketing managers can use GenAI to design and create cover page for reports, videos for customer presentations. Retail and institutional customers invest in equities, fixed income and other alternative assets through funds floated by buy side asset management (AM) firms. GenAI can be used to assist investors in choosing right products for short-term and long-term investments.

Research Analysts and product designers use a wide variety of knowledge documents, sell side research reports on securities, historical market data, macro- and micro-economic indicators along with real-time business news with market sentiments to write research reports for assisting fund managers in their decision-making process. They can create, back test and execute automated risk-adjusted investment strategies with GenAI. They can also find out the limitations in their investing strategies, to arrive at alternate diversified and profitable strategies.

Traders, portfolio managers and risk managers analyze complex data sets, including charts, graphs and financial statements. GenAI improves efficiency in following activities of portfolio management:

• Portfolio analysis through classification and categorization of investments in various perspectives such as geography, industry, sector and ESG parameters
• Personalized recommendations for portfolio holdings such as ETFs, stocks, cryptocurrencies, bonds, and mutual funds based on inputs from investment research
• Risk analysis covering liquidity, credit and market risks with respective confidence levels
• Tail risk analysis for special situations
• Create training data for hypothetical stress test scenarios
• Performance reports by way of storytelling for personalized investor communication, ESG analysis and reporting

Alternative asset managers look for high-impact use cases and private companies working on those use cases. Generative AI can assist in identifying emerging trends and disruptive technologies focusing on high-impact use cases. So alternative asset managers collect data across the globe to identify potential companies for investment. GenAI can be used to consolidate and compare information on companies across industries/ sectors. For further analysis, GenAI can structure the information in required format to perform competitive analysis.

Asset servicing firms/ fund administrators provide analytics capabilities through their data solution platforms for AM firms. The data solutions consolidate internal enterprise data which can be augmented with external data. GenAI can provide visibility into this data through a question-and-answer mechanism instead of business users pulling in specific data sets for custom analysis.

Customer service representatives at AM firms use their knowledge base of historical information to respond to user queries. GenAI can aid them by showing relevant responses in their screens while they are handling customer queries. This results in efficient resolution of issues, leading to improved customer satisfaction, lower costs and also quicker employee onboarding.

Internal communication in global organizations with language barriers can use GenAI for streamlining day-to-day tasks including information gathering in English.

Technology division of AM firms can use GenAI to generate and test code quickly and efficiently in order to boost efficiency at a scale.

Regulatory and other concerns

A general concern about GenAI is with respect to violation of Global Data Protection Regulation since it needs large amount of data for training which may include personal data. This can be mitigated by restricting the input data to LLMs used by AM firms for specific functions.

Regulators across US, Europe and China are proposing new rules to address protection of individual rights, use of copyrighted information, restrict AI generated content, additional transparency requirements, conflict of interest, accountability policy, etc.

The risk of GenAI providing biased responses or getting into ‘hallucination’, can be mitigated by tuning the model and installing ‘guardrails’.

Conclusion

As explained above, GenAI can help users across front-, middle-, and back-office functions of AM firms to augment existing capabilities in investment research, portfolio analysis, performance reporting, data and analytics for enhancing decision-making, improving efficiency and optimizing operations. GenAI will be well positioned in the technology landscape of AM firms after stabilization of the current hype cycle.