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GenAI: Revolutionizing digital transformation for fund services


Dimitri Tsopanakos:
 Partner, Deloitte UK
Simon Ramos: Partner, Deloitte Luxembourg
Cécilia Tondini: Manager, Deloitte Luxembourg



Performance Magazine Issue 44 - Article 6

To the point

  • Generative AI (GenAI), alone or paired with machine learning, can majorly transform the fund servicing industry. Existing examples across the value chain showcase its transformative impact, from operating model to process optimization.
  • Adopting GenAI requires strategic decision-making. Developing in-house capability provides control but demands significant resources. Acquiring third-party technology offers immediate expertise but introduces integration challenges. Using off-the-shelf products is efficient but might limit potential customization.
  • The complexities and associated risks of GenAI demand a comprehensive assessment. Organizations must address data biases, ethical concerns, misuse risks, accuracy challenges, and the evolving nature of GenAI's user experience. It’s important to assess measures like implementing a trustworthy GenAI framework and appropriate safeguards.
  • GenAI adoption also poses regulatory challenges. Financial firms must navigate evolving regulations like the EU AI Act, GDPR, and the AI Bill of Rights. These frameworks impose stringent requirements for AI usage, emphasizing data privacy and ethical standards. Careful attention is imperative to avoid legal complications and maintain regulatory compliance.

Compared to other types of artificial intelligence (AI), Generative Artificial Intelligence (GenAI) models stand out due to their wide-ranging capabilities and flexibility.

GenAI encompasses various AI models capable of creating original content, including but not limited to text, images, music, video, or code. In contrast, large language models (LLMs) are a specific application of Generative AI, focusing on generating and understanding human language through extensive text data, and excelling in tasks like text generation and comprehension.

These models can be particularly enhanced with two cutting-edge techniques: retrieval augmented generation (RAG) and fine-tuning. RAG combines generative capabilities with an ability to search for and incorporate relevant information from your knowledge base. Fine tuning is another technique that gives additional information to the LLM and retrains it on a specific task or dataset.

Nonetheless, to unlock GenAI’s full value, organizations must reimagine traditional processes by building upon digital, data, and cloud technology advancements and putting human adoption at the center of their transformation.

This article delves into the critical pain points observed in the market, such as regulatory compliance, fee pressure or sophisticated client demand, and showcases examples of how Generative AI can help tackle these challenges while mitigating its risks.

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.


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.


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.


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.

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