Garbage In – Garbage Out: The hallucination frustration

In today's financial markets, where each investment decision (or its absence) can significantly impact fund performance, the quality of investment insights has never been more crucial. Large Language Models (LLMs) have emerged as powerful tools for processing vast financial data, from market reports to regulatory filings. However, a persistent challenge threatens the reliability of these systems: hallucinations, or the generation of plausible but factually incorrect information. For investment professionals, these AI-generated inaccuracies aren't just technical glitches – they're potential catalysts for significant financial losses.

Recent incidents in the financial sector have highlighted how AI hallucinations can lead to costly mistakes. When LLMs generate incorrect market analysis or misinterpret critical financial data, the consequences ripple through investment decisions, risk assessments, and compliance processes. Investment managers have reported cases where AI systems provided outdated interest rate information or generated fictional market trends, leading to suboptimal portfolio allocations and missed opportunities.

While many technology providers focus on refining model architectures, Orbit’s analysis reveals that the root cause often lies in input data quality and the procedures to control LLM over input data. Consider how investment decisions are made: analysts rely on a complex web of information sources – earnings call transcripts, market data, economic indicators, news, and exchange filings. When this data enters an AI system in an inconsistent, outdated, or poorly structured format, even the most sophisticated LLM will struggle to generate accurate insights.

The financial industry faces unique challenges in this regard. The sheer volume of financial data generated daily, combined with the need for real-time analysis, creates an environment where data quality becomes paramount. MiFID II and other regulatory frameworks demand not just accuracy but also traceability in investment decisions. Poor quality data leading to AI hallucinations can, therefore, create both immediate financial risks and potential compliance issues.

This is where Orbit's approach makes a crucial difference for investment professionals. Our platform explicitly addresses these challenges by pre-processing and storing all financial documents in an LLM-ready format. Think of it as having a team of expert analysts who meticulously verify, standardise, and index

every piece of financial information

before it reaches your decision-making process.

For portfolio managers and investment analysts, this means:

- More reliable market insights derived from clean, well-structured data

- Reduced risk of making investment decisions based on hallucinated information

- Faster analysis of market opportunities without compromising accuracy

- Enhanced compliance with regulatory requirements through traceable data lineage

The impact on investment operations is significant. One global asset manager using our platform reported a 60% reduction in time spent validating AI-generated market analysis, while another noted improved accuracy in their automated financial report processing. These improvements translate directly to more efficient investment processes and better-informed decisions.

As the financial industry continues to embrace AI for investment analysis, we've discovered that reducing hallucinations requires a comprehensive approach beyond simply developing more sophisticated models. Our experience has revealed three critical factors that work in concert to deliver reliable AI-powered investment insights:

First, trustable, high-quality input data serves as the foundation. Just as investment professionals wouldn't make decisions based on unverified information, AI systems require meticulously vetted, standardised financial data. This means ensuring that market data, company filings, and numerical indicators are accurate, up-to-date, and properly contextualised before they enter any AI pipeline.

Second, superior PDF processing capabilities are essential. Financial documents often arrive in complex PDF formats, containing crucial information in tables, footnotes, and structured sections. Advanced PDF processing ensures that every piece of valuable data is accurately extracted and preserved, maintaining the integrity of financial information from source to analysis.

Third, enhanced LLM control mechanisms provide the necessary governance layer. By implementing robust control systems, we can guide LLMs to focus on relevant financial information, respect data hierarchies, and maintain consistency in their analytical outputs. This controlled environment significantly reduces the risk of hallucinations while increasing the reliability of AI-generated investment insights.

Orbit’s success in eliminating AI hallucinations demonstrates that the future of investment analysis depends not just on model sophistication but on the holistic management of the entire data pipeline. As markets become more complex and data volumes grow, this comprehensive approach will become increasingly crucial for maintaining competitive advantage in the investment landscape.