FinCatch utilizes knowledge graphs and fine-tuned large language models to analyze complex financial relationships and quantify the impacts of market events on asset prices. The platform addresses the challenge of information overload by providing structured insights that enable investors to make informed decisions based on clear, actionable data.
Funding
$330K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Individual investors and financial professionals face information overload and struggle to discern complex relationships within dynamic markets. Existing AI investment tools often lack the ability to examine connectivity, financial impacts, or link analyses to fundamentals, making it difficult to synthesize factors for optimal decision-making.
Solution
FinCatch utilizes knowledge graphs and fine-tuned large language models to analyze complex financial relationships and quantify the impacts of market events on asset prices. The platform connects daily events to their financial impacts and quantifies the magnitude of these effects, providing structured insights that enable investors to make informed decisions based on clear, actionable data. By automating complex tasks like data analysis, modeling, and reporting, FinCatch empowers autonomous decision-making on par with professionals.
Target Audience
FinCatch targets self-directed investors, asset and wealth management professionals, and decision-makers seeking advanced research tools to navigate complex financial markets.
Features
- Interactive knowledge graph visualization to uncover hidden investment opportunities.
- Quick impact summaries that condense vital event takeaways into concise briefs.
- Advanced modeling of long-term earnings effects to evaluate risk exposure and identify mispriced stocks.
- Information links between peers and external influences to discover hidden cross-event connections.
- Financial Knowledge Graph (KG) that organizes and connects financial data, capturing intricate relationships between entities and metrics.
- Fine-tuned Large Language Models (LLM) customized using specific financial use cases and data.
- Reinforcement Learning with Human Feedback (RLHF) to continuously improve model accuracy and reliability.