AI-powered document classification for fund management documentation processes
Fund management processes involve handling large volumes of financial and regulatory documents that need to be accurately identified, classified, and routed. The goal of this project was to explore how AI could automate document classification, reducing manual effort while improving efficiency and consistency.
Client
fundcraft
Project duration
2 months
Tools
Figma · Claude · Jira · Notion
Problem
Fund management teams rely on large amounts of documentation across different stages of their workflows. Documents often arrive through multiple channels and need to be manually reviewed and categorized before they can be processed.
This created several challenges:
Manual classification: teams spent valuable time identifying and categorizing incoming documents.
High volume: large numbers of documents made the process difficult to scale efficiently.
Risk of errors: inconsistent classification could lead to documents being misrouted or processed incorrectly.
Lack of visibility: users had limited insight into the status and classification of documents within the workflow.
Approach
The project focused on understanding the existing document management workflow and identifying opportunities where AI could reduce repetitive tasks without removing human oversight.
The main objectives were:
Understand the current classification process and user pain points.
Define a clear taxonomy for the different document types.
Explore how AI could automatically identify and classify incoming documents.
Design an interface that allows users to review, validate, and correct AI-generated classifications.
Create a transparent workflow that builds trust in AI-assisted decisions.
Solution
The proposed solution combines AI-powered classification with a human-in-the-loop workflow.
Automatic classification: AI analyzes incoming documents and assigns them to the appropriate category based on their content.
Confidence indicators: the interface communicates the AI's level of confidence, helping users identify cases that require attention.
Human validation: users can review, confirm, or change the suggested classification before the document continues through the workflow.
Centralized document management: classified documents are organized in a clear interface, making them easier to search, filter, and track.
Exception handling: documents that cannot be confidently classified are flagged for manual review rather than being automatically processed.
Outcome
The resulting experience demonstrates how AI can be integrated into complex financial workflows to automate repetitive classification tasks while keeping users in control.
By combining automation, transparency, and human validation, the solution aims to make document management faster, more consistent, and easier to scale, while reducing the cognitive load associated with repetitive manual classification.