GenAI: Revolutionizing digital transformation for fund services

Data & digitalization
 

By leveraging sentiment analysis, synthetic data generation, and automation capabilities of both machine learning (ML) and GenAI, organizations stand to transform their processes significantly.

• Enterprise-wide data search and access: Many organizations have data stored in multiple locations, either on premise or utilizing a cloud environment. GenAI can serve as the interface between search layers and data storage, returning synthetic data that retain the properties of the original datasets. Synthetic data has several uses, most of them related to testing, system refinement and training.
• Portfolio management: GenAI can process and synthesize real-time and historical market data, news articles, and financial data and then communicate about it in natural language; this would help portfolio managers identify trends, mitigate risk, and enrich investment strategies with informed recommendations.

Innovation
 

GenAI is the game changer in meeting increased client expectations and cost pressures. Specifically, it addresses these challenges through a variety of innovative approaches, including:

• Customer onboarding and management: GenAI can guide customers through onboarding steps, provide instantaneous responses to queries via LLMs, ensure necessary data is gathered seamlessly, and draft reports for new and existing clients.
• Hyper-personalized sales and marketing assistant: GenAI can rapidly generate customized marketing materials that not only ensure compliance with regulations, but match the messaging, tone, language, and cultural references of the target audience.

Operations
 

Machine learning is reshaping operations by making tasks more efficient and insightful; GenAI further enhances this by automating repetitive and time-consuming tasks, freeing up resources for strategic activities. These functionalities have led to its increased relevance in several investor services, including:

• Chatbots and virtual assistants: These tools can provide instant responses to customer queries. Generative AI can extend the capabilities of existing rules-based chatbots by providing natural language responses to queries that are beyond pre-programmed conversational pathways.
• AML and KYC: GenAI could take machine learning one step further by learning from typical, innate patterns of fraudulent behavior to generate synthetic training data. This data could  then be used to improve the accuracy of fraud detection and even create new fraud signals not yet known by the organization.
• Enhanced due diligence: GenAI can assist in due diligence by generating detailed risk profiles for individual clients or transactions. This enables asset servicers to tailor their activities according to the risk level.
• Exception handling: Operations personnel can use a simple language-based chat interface to navigate a complex database, helping identify reasons behind discrepancies or delays and suggests remediation strategies.
• Regulatory compliance: GenAI can automatically generate compliant, up-to-date documentation by learning from the latest information on regulatory changes. Consequently, organizations can adapt to regulatory changes more efficiently and effectively, thereby reducing the risk of error and non-compliance.
• Client reporting: Firms can produce insightful reports that are tailored to clients' individual needs and preferences for improved understanding and a more efficient communication process.

As employees familiarize themselves with prompt writing, they will be able to refine the model’s output to increase the accuracy of answers and train the system to handle a wider set of scenarios.

Product development
 

GenAI can assist front-office and distribution teams on multiple levels, such as:

• Deal sourcing: GenAI can summarize market data, news, and other sources into customized reports to help portfolio managers identify potential investment opportunities.
• Client profiling: The combined use of machine learning and Generative AI can help distribution teams provide better investment advice and attract more investors to the alternative asset classes.
• Client query assistance: Clients are confronted with several challenges when working toward compliance. GenAI can search through long, complex policies and regulations and provide users with natural language responses that are easier to understand.

How to mitigate certain limitations
 

• Hallucinations: Because GenAI searches through a broad spectrum of information that includes policies, regulations, and papers, there is a potential risk that it returns inaccurate or non-existent information. This phenomenon descends from the transformer architecture on which large language models (LLMs) are currently built. A way to mitigate or limit this risk would be to use the best LLM models available in the market (e.g., GPT4), design and track key performance indicators (KPIs) related to output quality, and have the output reviewed by a human.
• Limited creativity: Users must be aware of initial AI’s limited capabilities. Since it is strictly confined to its training data, it is unable to generate content beyond its training scope. To overcome this obstacle, organizations should seek to create collaborative environments where human expertise complements AI; people will be critical to feeding AI models with industry-specific information, enabling them to provide increasingly refined and relevant outputs, which should then also be reviewed by humans.
• Privacy: When dealing with client information, privacy is key. Therefore, organizations should use secure, encrypted channels for transmitting data between AI systems. They should also consider in-house AI processing for particularly sensitive data. Finally, educating users on how to safely engage with LLMs will be essential for mitigating data confidentiality risks.
• Data ownership: In addition to the privacy concerns, data ownership also demands significant attention: Who is the data owner, and who has the right to use the data? These potential issues can be mitigated by ensuring contracts and agreements clearly define the ownership of AI-generated content and data; implementing a second LLM to manage and track data usage rights and generated content; and assessing copyright infringements.
• Output reliability: Furthermore, relying on GenAI to ensure compliance and provide accurate information through automated outputs might produce random, or less reliable, responses. To tackle this challenge, a LLM providing response ratings could be developed to evaluate the relevance and accuracy of AI outputs, with feedback loops for continuous improvement. Additional layers of AI that specialize in contextual analysis could also be implemented to filter out irrelevant responses.
• Biased data: Finally, existing bias in AI models (for example, AI trained on biased data) can lead to unbalanced or non-sensical outputs in both client query assistance and reporting process automation. This risk could be mitigated by: 1) deploying bias detection tools and methodologies to detect and correct output biases; and 2) curating the training data set along with providing ample amounts of data.

Conclusion
 

Generative Artificial Intelligence stands as a powerful innovation to entrenched challenges within the funds industry, paving the way for growth and innovation. Recognizing it not merely as a trend, but as a significant structural technology, positions organizations to thrive in an era where adaptive technologies drive industry transformation. Financial institutions, especially asset servicers, cannot afford to ignore GenAI: Its adoption will be key to staying competitive, fostering innovation, and unlocking new avenues for growth in the years to come.