The Problem – Hallucinations in AI and why they're dangerous in finance
In the asset management industry, AI hallucinations—where models generate plausible but factually incorrect information—pose significant risks to investment decisions and client trust. Traditional language models without proper grounding mechanisms can produce convincing yet fabricated market insights, potentially leading to costly investment errors, regulatory violations, and reputational damage. The stakes are particularly high in finance, where decisions impact billions in assets and must adhere to strict compliance standards.
The Solution – What RAG is and how it fixes the problem
Retrieval-Augmented Generation (RAG) represents a transformative approach in asset management, primarily aimed at reducing AI hallucinations and enhancing the accuracy of investment insights. By grounding AI responses in verified documents and data sources, RAG systems ensure outputs are factually accurate and traceable to their origins.
The adoption of RAG systems among top-tier asset management firms has increased significantly from 2024 to 2025. This surge underscores the industry's recognition of the risks posed by traditional language models without retrieval augmentation. Notably, Zero Gravity Marketing emphasized on April 25, 2025, how RAG significantly reduces guesswork by grounding answers in actual retrieved documents.
Daizy's Approach – Hybrid RAG, deterministic results, compliance-first design
Daizy has pioneered a hybrid RAG approach that combines multiple retrieval methods to ensure comprehensive and accurate information gathering. Our system employs:
- Deterministic processing that produces consistent, verifiable results for the same queries
- A compliance-first architecture to support clients' regulatory reporting efforts
- Multi-source verification that cross-references information across diverse financial documents
- Transparent citation mechanisms that allow users to trace every insight to its source
In practical applications, Daizy's RAG system has demonstrated exceptional performance in regulatory reporting and sales enablement scenarios.
By addressing the challenges such as conflicting information from credible sources and rapidly changing market conditions, Daizy continues to refine its RAG implementation to move closer to the goal of zero hallucinations in financial AI.
