FundEvolve – AI for Financial Compliance in Video Content
Country : Canada
Industry : FinTech
Team Size : 8+
Overview
Developed AI-Driven Compliance Watchdog for Financial Video Content:
We engineered a regulatory AI system capable of automatically detecting, analyzing, and flagging misleading or illegal financial advice in online video platforms such as YouTube and TikTok.
Ensured Legal Alignment Under Canadian Regulations:
Using an advanced Retrieval-Augmented Generation (RAG) architecture, the system cross-references detected content with Canadian financial laws and compliance guidelines to ensure accuracy and authenticity.
Client
FundEvolve is a Canada-based financial technology company focused on improving transparency in digital financial education. The organization monitors and validates financial content published across major online video platforms to help users access trustworthy and compliant financial advice.
Product
The product is an AI-powered compliance engine that analyzes and validates financial advice shared in user-generated videos.
It processes video content from multiple platforms, transcribes the audio, detects financial advice, and matches the extracted information against Canadian financial regulations. The system then automatically flags and reports non-compliant or misleading content.
Key Components:
Automated Video Scraper integrated with YouTube and TikTok APIs
Speech-to-Text Transcription Module for converting video audio into searchable text
NLP Engine for detecting financial advisory statements
RAG-Based Legal Matcher to verify content alignment with financial laws and compliance standards
Compliance Dashboard for reviewing flagged content and generating reports
Goals and Objectives
Automate Financial Content Review:
Reduce the manual effort required to review large volumes of online financial videos.
Enhance Regulatory Compliance:
Ensure that detected advice aligns with the Financial Consumer Agency of Canada (FCAC) and other Canadian financial authorities’ guidelines.
Build Public Trust:
Create a transparent and trustworthy system for identifying authentic financial advice online.
Expertise Involved
Artificial Intelligence (AI) & Machine Learning (ML)
Natural Language Processing (NLP)
Regulatory Technology (RegTech)
Data Engineering
API Integration
Project Challenges
1. Complex Data Sources:
The system needed to process unstructured video content from multiple sources, each with different audio and metadata quality.
2. Detecting Implicit Financial Advice:
Many creators deliver financial guidance indirectly or conversationally, making accurate NLP interpretation a significant challenge.
3. Compliance Mapping:
Mapping extracted advice against detailed Canadian financial laws required a precise RAG-based legal reasoning pipeline.
Solution
As the first step, our AI team designed a scalable architecture centered around a Retrieval-Augmented Generation (RAG) framework.
Core System Architecture:
1. Content Extraction:
Implemented automated pipelines for downloading and transcribing video content using Whisper-based ASR models.
2. Financial Advice Detection:
Fine-tuned NLP models (based on transformer architectures such as BERT and RoBERTa) to identify segments containing direct or indirect financial guidance.
3. Regulatory Matching:
Developed a RAG-driven legal knowledge base, integrating a corpus of Canadian financial laws, investment guidelines, and disclosure policies. This component validates detected statements by retrieving relevant clauses and matching them against advisory claims.
4. Flagging & Reporting:
Created a rule-based decision layer that categorizes flagged content by severity (informational, warning, or critical) and automatically generates structured compliance reports.
Tech Stack
AWS Cloud Infrastructure
Python & FastAPI
OpenAI GPT Models for Text Reasoning
Whisper ASR for Transcription
Vector Database (Pinecone) for Legal Data Retrieval
LangChain Framework for Orchestration
PostgreSQL for Metadata Storage
Process
Step 1
Business Analysis
Conducted an in-depth review of FundEvolve’s compliance objectives, mapping out required Canadian legal frameworks and identifying data sources (YouTube API, TikTok API, financial law repositories).
Step 2
Architecture Design
Developed a modular RAG architecture to ensure efficient query handling and scalable integration with external data sources.
Step 3
Model Training & Integration:
Fine-tuned pre-trained transformer models for detecting financial discourse and integrated them with the RAG compliance layer.
Step 4
Testing & Validation:
Performed black-box testing and “compliance validation tests” using curated video datasets to assess detection accuracy and false positive rates.
Step 3
Deployment & Documentation:
Packaged the entire solution as a microservice accessible through an API, accompanied by comprehensive integration documentation for FundEvolve’s technical team.
Results
The developed solution transformed FundEvolve’s ability to monitor and validate financial video content at scale.
- Automated detection of potentially misleading or illegal financial statements across multiple platforms.
- Improved compliance accuracy through RAG-based legal matching.
- Increased transparency for Canadian audiences seeking trustworthy financial information.
- Streamlined compliance operations by reducing manual review efforts by over 70%.