The project is protected by a Non-Disclosure Agreement (NDA); visual designs, links to prototypes, or some crucial parts of it may not be permissible to share publicly.
Overview
Cutting-edge AI technology to offer a 360 Holistic View of financial landscapes, providing insights, connecting investors, wealth managers and institutions, as well as next best actions to optimize your customers’ wealth.
Problem Statement
“Financial Advisors managing High-Net Worth Individuals’ investments face challenges in extracting, validating, and structuring data from complex ‘fund subscription agreement‘ documents.”
User Flow
User flow starting when the system prompts for a document to be uploaded, and ending when the user saves & uploads the data.
Analysis
Implementation Analysis
Canoe Intelligence
Canoe Intelligence offers an advanced AI-based document extraction feature that streamlines the process of extracting data from alternative investment documents. Here are the key highlights:
AI-Driven Document Extraction
Canoe Intelligence utilizes artificial intelligence and machine learning techniques, rather than traditional OCR, to accurately extract data from documents. This includes:
Natural Language Processing (NLP) to understand the context and meaning of text
Text anchoring to identify relevant data points within documents
Ability to learn and create reliable patterns for scalable data extraction
This AI-driven approach enables Canoe to extract data efficiently, regardless of the document’s structure or length.
Multi-Allocation Extraction
Canoe can extract relevant data points from reporting documents containing multiple allocations, such as distribution notices, capital call notices, or account statements with data from multiple clients, entities, or investments. By configuring the appropriate attributes, users can access all the required data without restrictions to a single commitment or subscription.
Automated Document Categorization
Canoe’s platform automatically categorizes and tags ingested documents based on their type (e.g., account statements, capital call notices, quarterly reports) using AI recognition. This eliminates the need for manual document classification.
Data Validation
Extracted data is validated against predefined business, accounting, and investment rules to ensure accuracy. This includes checks for discrepancies, such as mismatched beginning/ending balances or missing capital calls.
By leveraging AI and automation, Canoe Intelligence streamlines the document extraction process, enabling investment firms to access accurate and timely data from alternative investment documents efficiently.
Interface Considerations
Clarity: The interface should clearly display the retrieved information for validation. The data fields should be labeled accurately to avoid confusion.
Ease of Use: The interface should allow easy modification of the data. This could be achieved through editable fields.
Guidance: The interface should guide the user through each step of the validation and modification process. This could be achieved through prompts or tooltips.
Error Handling: The interface should provide clear error messages if the data entered is invalid and suggest the correct format.
Confirmation: The interface should ask for confirmation before saving and uploading the data to prevent accidental data loss.
Solution
Document Upload Screen: This screen allows the user to select and upload the documents. It should include an upload button and a list of previously added information.
Uploaded Documents Screen: This screen displays the list of successfully uploaded documents and failed uploaded with error message as well.
Data Retrieval and Validation Screen: This screen displays the retrieved data for each document. It should include labeled fields for each piece of data. It allows the user to validate, modify, or add data. It should include editable fields and a confirmation button.
Save Data/Summary Screen: This screen allows the user to save and upload the data. It should include a save/proceed button.
Instructions for Implementation: The development team should ensure that the system can accurately analyze the documents and retrieve the required data. They should also implement error handling for invalid data and a confirmation prompt for data upload.
