Current Problems faced by Market

Most of the users does not get the full context of the news articles generated for an financial institution.

High Temporal Sensitivity

Financial data is highly sensitive to time. A slight delay in the analysis or utilization of this data could lead to missed opportunities or faulty decision-making.

Lack of Personalization

Many financial services lack customization, leading to less-than-optimal advice for individuals or organizations with unique risk profiles and financial goals.

High Adaptation Costs

The integration of AI solutions into the financial sector can be costly, and the fine-tuning of large language models (LLMs) can require significant computational resources.

Low Signal-to-Noise Ratio

Financial data often contains a large amount of 'noise', or irrelevant information, which makes it challenging to extract meaningful insights.

Dynamic Financial Landscape

The financial world is continuously changing, which makes it difficult to build models that can stay relevant over time. Models must constantly adapt to evolving market conditions.

Data Access and Management

Access to high-quality financial data is crucial for training effective models. However, curating and managing this data can be complicated and time-consuming.