Our Core Values
# We Develop AI
# We Demystify Analysis
# We Deliver Financial Insights
FinCatch
AI Investment Research Intelligence
We take steps ahead to make information linkages visible and financial impact measurable to drive actionable insights for self-directed investors, asset and wealth management professionals, and decision makers.
Average
+ $2,473.65
Performance
Quarter 🔻
Expenses
$123,456
Income
$654,321
Profit
+ $530,865
Our Unique Features
Through a combination of knowledge graph and machine learning models, FinCatch analyzes relationships between financial events, fundamentals, earnings and other market influences to better understand price movements. The platform not only connects daily events to their financial impacts, but also quantifies the magnitude of these effects.
Features include knowledge graph visualization, quick event summaries with quantified impacts, and in-depth analysis of direct and indirect financial consequences. The platform empowers users to make well-informed investment decisions based on a clearer view of true market dynamics and valuation.
1
Interactive knowledge graph visualization
empowers users to efficiently search and uncover hidden investment opportunities within the deluge of market news and information.
2
Quick impact summaries
condense vital event takeaways into bite sized briefs to save analysts from spending excessive time researching changes relevant to portfolio holdings and coverage industries.
3
Advanced modeling of long-term earnings effects
outfits asset managers with rigorous analytical tools to better evaluate risk exposure and identify mispriced stocks for strategic recalibration of portfolio compositions.
4
Information links between peers and external influences
give users a competitive edge to discover hidden cross-event connections fueling alpha insights.
What Problem Are We Solving for Our Users?
The rise of online brokerages and robo advisors has made investing far more accessible for individuals, yet a gap remains in advanced research tools. FinCatch aims to fill this need by automating complex tasks like data analysis, modeling, and reporting to empower autonomous decision-making on par with professionals. Investors also are increasingly seeking more autonomy and control over their portfolios, beyond traditional advisory models.
The users grapple with:
1
Information overload from myriad data sources
2
Difficulties discerning complex information relationships
3
Constant volatility from dynamic markets
4
Insufficient time to synthesize all factors for optimal decision-making
What Makes Us Unique?
While other AI investment tools focus on information grouping, technical analysis, or language model-based searching, they lack the ability to examine connectivity, financial impacts, or link analyses to fundamentals. Our research platform bridges these gaps by predicting impacts, tracing information relationships, and facilitating smarter investing through automated and foundational research capabilities. The platform benefits both individuals lacking knowledge and professionals overwhelmed with information overload and market noise.
We effectively analyzes interconnected data and complex financial relationships to offer users efficient access to:
1
Readable Financial Analysis
2
Quantified Event-driven Impact
3
Insightful Broader Implication
Our Core Technologies
Financial KG – RAG
X
Fine-tuned LLM with RLHF
X
Specialized Predictive ML
FinCatch’s technology niche lies in the innovative fusion of advanced technologies, including knowledge graph (KG), fine-tuned large language model (LLM), and reinforcement learning with human feedback (RLHF). This unique combination positions FinCatch as a leader in the financial intelligence sector, enabling it to deliver unparalleled insights and comprehensive financial analysis that surpass traditional approaches.
1
The financial KG employed by FinCatch organizes and connects financial data, enabling a contextual understanding by capturing intricate relationships between entities and metrics. This comprehensive view allows users to go beyond traditional analysis methods and gain a holistic understanding of the financial landscape.
2
FinCatch utilizes fine-tuned large language models, which have a deep understanding of language and context. These models are further customized using specific financial use cases and data, enhancing their specialization in analyzing financial information.
3
Human experts provide feedback and corrections to the model’s outputs, which are then used to update and improve the models. This integration of human expertise ensures that the language models align with domain-specific knowledge, resulting in more accurate and reliable results. Additionally, RLHF enables continuous learning and refinement of the models, enhancing their performance over time.
| Functionality | LLMs | FinCatch |
| Information Retrieval and Summarization | ✔️ | ✔️ |
| Question-Answering | ✔️ | ✔️ |
| Sentiment Analysis | ✔️ | ✔️ |
| Technical Analysis | ❌ | ✔️ |
| Financial Analysis | ❌ | ✔️ |
| Reasoning and Inference | ❌ | ✔️ |
| Impact Estimation | ❌ | ✔️ |
Awards & Features
