Large language models are changing how investment professionals find, consume, and analyze information. But as financial institutions build AI into their investment research workflows, one thing is becoming increasingly clear:
A better model alone does not guarantee better financial research.
The quality of an AI-generated answer depends just as much on the information available to the model and the guidance it receives as it does on the model itself.
Across a series of proprietary studies, Aiera has examined each of these variables independently. Taken together, that research points to three foundational factors that have a measurable impact on the quality of AI-generated financial research:
- Selecting the right foundational model for the task
- Grounding it in professional-grade, AI-ready financial content and data
- Guiding it to analyze and communicate like an experienced financial analyst
The result is a more practical framework for evaluating financial AI, one based on the work investment professionals actually need AI to perform.
General AI Rankings Don’t Tell the Whole Story
Most public AI benchmarks measure broad capabilities such as reasoning, coding, mathematics, and general knowledge. Those evaluations are useful, but institutional financial research presents a very different set of challenges.
An AI system may need to retrieve information from proprietary sources, synthesize evidence across multiple documents, distinguish material developments from background information, and produce an answer that is complete, accurate, and grounded in source material.
Aiera’s research found that performance on those tasks does not necessarily track with general AI rankings. That distinction matters. Choosing a model because it ranks highly on a broad benchmark does not mean it will be the strongest model for financial research.
Instead, foundational models should be tested against the specific research workflows they will actually be expected to perform.
Factor 1: Models
Select the Right Foundational Model for the Task
The first part of the equation is model selection.
The Aiera Leaderboard evaluates leading foundational models specifically on the financial research capabilities that matter to institutional investors, placing greater weight on accurate, source-grounded research than on general AI capability.
The research shows that financial research performance is a distinct capability. Models that perform well on broad benchmarks do not necessarily maintain the same relative performance when asked to complete real financial research tasks.
For financial institutions, the implication is straightforward: there is no substitute for evaluating models in the context in which they will actually be used.
The question is not simply, “What is the best AI model?” It is, “What model performs best for this financial research workflow?”
Factor 2: Data
Ground AI in Professional-Grade Financial Content
Even the right model can only work with the information it can access. And for financial research, much of the information analysts need simply does not exist on the public web.
Aiera’s Lift research found that 78% of analyst-style financial questions required information unavailable through open-web sources. Connecting models to Aiera’s permissioned financial content through the Model Context Protocol, or MCP, materially improved performance, with top-performing models capturing 2.4 times more of the required key facts.
The largest gains came from areas where proprietary information matters most, including professional research, analyst theses, and questions requiring synthesis across multiple documents.
This changes the way financial institutions should think about AI performance.
When an AI system produces an incomplete answer, the problem may not be that the model is incapable of answering the question. It may simply lack the information required to do so.
For institutional financial research, content is not an accessory to the model. It is a fundamental part of the system.
Factor 3: Guidance
Guide AI to Analyze and Communicate Like an Experienced Financial Analyst
Access to the right information is still not enough.
Investment professionals expect research to do more than retrieve facts. Answers need to identify what matters, synthesize information clearly, provide appropriate context, and communicate in a way that reflects the rigor and structure of professional financial analysis.
Aiera studied approximately 12,800 professional financial research documents to identify recurring analytical and writing patterns. Rather than manually prescribing how an AI system should respond, Aiera used those patterns to derive a global financial research style template.
The results showed that a single corpus-derived template could outperform numerous specialized templates. Independent AI judges preferred templated answers up to 90.5% of the time, and those answers captured more of the important facts required to fully address the research question.
The takeaway extends beyond writing style.
Effective guidance helps determine how a model approaches the research itself: what information it prioritizes, how it synthesizes evidence, and how completely it addresses the question being asked.
A Better Framework for Financial AI
Model, data, and guidance are often evaluated separately.
In practice, they work together.
A powerful foundational model without access to professional financial information will encounter questions it simply cannot answer. A model with the right data but poor guidance may retrieve the necessary information without synthesizing it effectively. And sophisticated prompting cannot compensate for choosing a model that performs poorly on the underlying research task.
The strongest financial AI systems combine all three:
Models
Select foundational models based on the task and real financial research workflows.
Data
Ground AI in professional-grade, AI-ready financial content and proprietary data.
Guidance
Guide AI to analyze and communicate like an experienced financial analyst.
Together, these factors provide a more meaningful way to evaluate AI for institutional finance than model size or general benchmark rankings alone.
Rethinking How Financial AI Should Be Evaluated
As financial institutions move from experimenting with generative AI to deploying it within real workflows, the question is shifting. It is no longer simply whether AI can support financial research.
The more important question is what makes financial AI reliable enough to support the work investment professionals actually do?
Aiera’s research suggests that the answer cannot be reduced to a single model or benchmark score.
Better financial AI requires the right model for the task, access to the information the task requires, and guidance derived from the way professional financial research is actually conducted.
Those are the factors that determine whether an AI system simply generates an answer or produces research that is accurate, complete, grounded, and useful.
Download The Aiera Difference: How to Use AI for Financial Research to explore the research behind all three foundational factors.
The Aiera Difference: What It Takes to Build Better AI for Financial Research
Large language models are changing how investment professionals find, consume, and analyze information. But as financial institutions build AI into their investment research workflows, one thing is becoming increasingly clear:
A better model alone does not guarantee better financial research.
The quality of an AI-generated answer depends just as much on the information available to the model and the guidance it receives as it does on the model itself.
Across a series of proprietary studies, Aiera has examined each of these variables independently. Taken together, that research points to three foundational factors that have a measurable impact on the quality of AI-generated financial research:
The result is a more practical framework for evaluating financial AI, one based on the work investment professionals actually need AI to perform.
General AI Rankings Don’t Tell the Whole Story
Most public AI benchmarks measure broad capabilities such as reasoning, coding, mathematics, and general knowledge. Those evaluations are useful, but institutional financial research presents a very different set of challenges.
An AI system may need to retrieve information from proprietary sources, synthesize evidence across multiple documents, distinguish material developments from background information, and produce an answer that is complete, accurate, and grounded in source material.
Aiera’s research found that performance on those tasks does not necessarily track with general AI rankings. That distinction matters. Choosing a model because it ranks highly on a broad benchmark does not mean it will be the strongest model for financial research.
Instead, foundational models should be tested against the specific research workflows they will actually be expected to perform.
Factor 1: Models
Select the Right Foundational Model for the Task
The first part of the equation is model selection.
The Aiera Leaderboard evaluates leading foundational models specifically on the financial research capabilities that matter to institutional investors, placing greater weight on accurate, source-grounded research than on general AI capability.
The research shows that financial research performance is a distinct capability. Models that perform well on broad benchmarks do not necessarily maintain the same relative performance when asked to complete real financial research tasks.
For financial institutions, the implication is straightforward: there is no substitute for evaluating models in the context in which they will actually be used.
Factor 2: Data
Ground AI in Professional-Grade Financial Content
Even the right model can only work with the information it can access. And for financial research, much of the information analysts need simply does not exist on the public web.
Aiera’s Lift research found that 78% of analyst-style financial questions required information unavailable through open-web sources. Connecting models to Aiera’s permissioned financial content through the Model Context Protocol, or MCP, materially improved performance, with top-performing models capturing 2.4 times more of the required key facts.
The largest gains came from areas where proprietary information matters most, including professional research, analyst theses, and questions requiring synthesis across multiple documents.
This changes the way financial institutions should think about AI performance.
When an AI system produces an incomplete answer, the problem may not be that the model is incapable of answering the question. It may simply lack the information required to do so.
For institutional financial research, content is not an accessory to the model. It is a fundamental part of the system.
Factor 3: Guidance
Guide AI to Analyze and Communicate Like an Experienced Financial Analyst
Access to the right information is still not enough.
Investment professionals expect research to do more than retrieve facts. Answers need to identify what matters, synthesize information clearly, provide appropriate context, and communicate in a way that reflects the rigor and structure of professional financial analysis.
Aiera studied approximately 12,800 professional financial research documents to identify recurring analytical and writing patterns. Rather than manually prescribing how an AI system should respond, Aiera used those patterns to derive a global financial research style template.
The results showed that a single corpus-derived template could outperform numerous specialized templates. Independent AI judges preferred templated answers up to 90.5% of the time, and those answers captured more of the important facts required to fully address the research question.
The takeaway extends beyond writing style.
Effective guidance helps determine how a model approaches the research itself: what information it prioritizes, how it synthesizes evidence, and how completely it addresses the question being asked.
A Better Framework for Financial AI
Model, data, and guidance are often evaluated separately.
In practice, they work together.
A powerful foundational model without access to professional financial information will encounter questions it simply cannot answer. A model with the right data but poor guidance may retrieve the necessary information without synthesizing it effectively. And sophisticated prompting cannot compensate for choosing a model that performs poorly on the underlying research task.
The strongest financial AI systems combine all three:
Together, these factors provide a more meaningful way to evaluate AI for institutional finance than model size or general benchmark rankings alone.
Rethinking How Financial AI Should Be Evaluated
As financial institutions move from experimenting with generative AI to deploying it within real workflows, the question is shifting. It is no longer simply whether AI can support financial research.
The more important question is what makes financial AI reliable enough to support the work investment professionals actually do?
Aiera’s research suggests that the answer cannot be reduced to a single model or benchmark score.
Better financial AI requires the right model for the task, access to the information the task requires, and guidance derived from the way professional financial research is actually conducted.
Those are the factors that determine whether an AI system simply generates an answer or produces research that is accurate, complete, grounded, and useful.
Download The Aiera Difference: How to Use AI for Financial Research to explore the research behind all three foundational factors.