Motivation

The motivation for developing the Investment Research Assistant stems from the need for Relationship Managers (RMs) in banks to efficiently query a stock universe and provide comprehensive investment advice to customers. Traditionally, RMs must navigate through a cumbersome click interface to find relevant stocks, particularly when specific criteria such as sector interest or geographical focus are involved. Furthermore, once stocks are identified, RMs need to delve into fact sheets to provide detailed rationales for each recommendation, explaining why a customer should invest in specific stocks or bonds. This process is time-consuming and labor-intensive. The Investment Research Assistant leverages GenAI to simplify this workflow, allowing RMs to use natural language queries and automate the extraction of relevant stock data and fact sheet insights.

Goal

The goal of the Investment Research Assistant, developed by Unique, is to streamline the process of querying a stock universe and preparing detailed investment recommendations. By integrating GenAI capabilities, it enables RMs to efficiently generate queries, extract pertinent stock data, and produce comprehensive rationales from fact sheets. This reduces the time and effort required to provide high-quality investment advice, enhancing the overall efficiency and effectiveness of RMs.

Structure and Logic of Assistant

Step 1: Extraction of Stock Data

The process begins when the RM inputs a natural language query specifying the customer's interests and criteria (e.g., "My client is interested in stocks with a 5 star Sustainability Rating"). The system then generates a query to an in-house or CSV-based stock database. The system extracts the most relevant stocks based on the given criteria, considering factors such as sustainability ratings or buy signals.

→ The extracted list of stocks is then prepared for further analysis.

Step 2: Extraction of Fact Sheet Rationales

In this step, the system loads the fact sheets of the stocks identified in Step 1 into an LLM (Large Language Model). The LLM processes each fact sheet to extract the most relevant rationales, providing detailed insights and justifications for why each stock is a suitable investment. The extracted rationales are then compiled into a coherent narrative.

→ The compiled rationales are presented in a chat interface, allowing the RM to review and refine the recommendations.

Step 3: Generating Follow-up Email

Once satisfied with the extracted stock data and rationales, the RM can generate a follow-up email to the customer. This email includes a detailed investment story, attaching relevant fact sheets and any additional necessary documents. The RM can customize the email content and attachments as needed.

→ The final email, complete with fact sheets and recommendations, is ready to be sent to the customer.

Possible Adaption of Assistant

The Investment Research Assistant can be adapted to various scenarios, enhancing its flexibility and applicability:

• Custom Data Integration: Users can upload their own stock universe as a CSV file and attach their own fact sheets for analysis.

• Tailored Outputs: The assistant can be customized to generate different types of outputs based on specific user needs, such as creating tailored investment stories.

• Custom Query Optimization: The system allows for the integration of custom few-shot learning examples to improve the accuracy of query generation.

Required and optional modules

The following modules are required/optional for this assistant: