Generative AI is transforming the financial services industry, streamlining complex processes and driving operational efficiency. At Kristal.AI, I spearheaded the development of the Fund Approval Form Generator, a groundbreaking solution that leverages Generative AI to address critical pain points in the fund due-diligence process. This article explores the problem, the innovative solution we built, and the comprehensive product management approach that ensured its success. As the first in a series on AI-driven financial innovation, this piece highlights how strategic prioritization, rigorous requirement gathering, and cutting-edge technology can deliver measurable impact.
The Problem: Inefficiencies in Fund Due Diligence
The Financial Products team at Kristal.AI faced significant challenges in onboarding new financial products. The due-diligence process required analysts to sift through hundreds of pages of dense documentation to extract relevant information, a task that was both time-consuming and prone to errors. Key issues included:
These challenges not only slowed down the onboarding process but also strained team resources and increased operational risks. As Head of Product, I recognized the need for a scalable, technology-driven solution to transform this process.
Prioritizing the Fund Approval Form Generator
To prioritize this project, I employed a structured decision-making framework, balancing impact, feasibility, and alignment with Kristal.AI’s strategic goals. The decision was driven by:
To quantify the potential impact, I conducted a time-and-motion study with the Financial Products team, estimating that automation could reduce processing time by 80%, saving approximately 120 man-hours per month. Additionally, error rates, previously at 15% due to manual oversight, could be reduced to near zero with AI-driven validation.
| Metric | Before | Projected After |
|---|---|---|
| Time per Fund (hours) | 10 | .50 |
| Monthly Man-Hours | 120 | 6 |
| Error Rate | 15% | <1% |
| Compliance Audit Prep Time | 20 hours | 1 hours |
Table 1: Estimated Impact of the Fund Approval Form Generator
Requirement Gathering and Solution Design
As the product manager, I led a collaborative requirement-gathering process to ensure the solution addressed the team’s needs while leveraging cutting-edge technology. The process included:
Based on the requirements, I proposed a solution using AWS Bedrock for Generative AI, Amazon OpenSearch Serverless Vector Store for efficient data retrieval, LangChain for orchestration, and S3 buckets for secure storage. This stack was chosen for its scalability, cost-effectiveness, and seamless integration with Kristal.AI’s existing AWS infrastructure.
Crafting User Stories and Driving Development
To translate requirements into actionable development tasks, I crafted detailed user stories that captured the needs of analysts, compliance officers, and auditors. Examples include:
These user stories were prioritized using the MoSCoW framework (Must-have, Should-have, Could-have, Won’t-have) to focus on critical features first. I worked closely with the engineering team to define acceptance criteria and ensure alignment with business objectives.
To manage the project, I adopted an Agile methodology, leading daily stand-ups, sprint planning, and retrospectives. I also collaborated with the UX team to design an intuitive interface for uploading documents and reviewing AI-generated outputs, ensuring a seamless user experience.
Implementation and Technology Stack
The Fund Approval Form Generator was built using the following components:
I worked with the engineering team to define the system architecture, ensuring scalability and compliance with data privacy regulations. Regular testing and feedback loops with the Financial Products team helped refine the AI model’s accuracy, achieving a 95%+ success rate in extracting relevant data.
Figure 2: System Architecture of Fund Approval Form Generator (Note: instead of Redis, we used S3 buckets)
Results and Impact
The Fund Approval Form Generator delivered transformative results:
| Outcome | Before | After |
|---|---|---|
| Funds Onboarded/Month | 12 | 18 |
| Audit Non-Compliance Issues | 3 per quarter | 0 per quarter |
| Team Satisfaction (1-5) | 3.2 | 4.8 |
Table 2: Measurable Outcomes Post-Implementation
Output tool snapshot
My Role as Product Manager
As Head of Product, I owned the end-to-end lifecycle of this project, from ideation to deployment. My contributions included:
Looking Ahead
The Fund Approval Form Generator is just the beginning of Kristal.AI’s journey with Generative AI. Future iterations will explore advanced features like real-time compliance checks and predictive analytics for fund performance. This project exemplifies how strategic product management, combined with innovative technology, can drive efficiency and scalability in financial services.
Stay tuned for the next installment in this series, where we’ll dive deeper into building AI-driven solutions on AWS, including practical tips for implementation and optimization.