Introduction to the FrontierFinance Benchmark

FrontierFinance is a long-running computer usage benchmark for real-world financial tasks, consisting of 25 complex financial modeling tasks, each requiring an average of over 18 hours of professional human effort. Its core purpose is to evaluate the performance of LLMs in real-world financial professional scenarios, bridge the gap between existing benchmarks and actual professional needs, and provide rigorous references for the application of AI in the financial field.

Background: AI Replacement Anxiety and Existing Issues in Financial Evaluation

With the improvement of LLM capabilities, AI replacement anxiety in knowledge-intensive industries (such as finance) has intensified, but there is a huge gap between existing benchmarks and actual needs:

• Most evaluations focus on simplified tasks such as short text Q&A and code generation, which are difficult to reflect the complexity of real work;
• Existing evaluations in the financial field have limitations such as simplified tasks, detachment from actual processes (ignoring tool usage), lack of professional standards, and short-term orientation;
• There is a lack of clear accountability mechanisms, making it difficult to quantify the responsibility and severity when models make errors.

Design and Task Composition of FrontierFinance

Core Design Principles

Follows five principles: long-running tasks, professional orientation, tool usage, structured evaluation, and human benchmarking.

Task Composition

Consists of 25 complex financial modeling tasks covering five areas: valuation modeling, financial forecasting, risk analysis, portfolio management, and derivative pricing, designed based on real business scenarios.

Workload Evaluation

Each task requires an average of over 18 hours of professional human effort, covering the entire process of data collection and cleaning, model building, calculation verification, and report writing, highlighting the project-level complexity of the tasks.

Evaluation Methods: Professional Standards and Human Benchmarks

Scoring Standard Development

Collaborated with financial professionals to develop a multi-dimensional scoring table, with evaluation dimensions including accuracy, completeness, clarity, professionalism, and usability (client-ready standards).

Human Benchmark Establishment

Experienced financial experts were hired to participate in task definition, scoring standard formulation, personally execute tasks to establish baselines, and participate in scoring LLM outputs to ensure evaluation credibility.

Key Findings: Human Experts Still Have Significant Advantages in Financial Tasks

Core Conclusions

1. The average score of human experts is significantly better than that of current state-of-the-art LLMs;
2. Humans are more likely to produce outputs that meet the "client-ready" standard;
3. LLM errors are more hidden (e.g., incorrect assumptions but correct calculations), leading to higher risks.

LLM Weaknesses

• Insufficient proficiency in tool usage (low efficiency in complex Excel modeling);
• Lack of judgment on the rationality of assumptions;
• Difficulty in iterative correction;
• Professional expression lacks the texture of industry reports.

Significance and Implications: Application Directions of AI in the Financial Field

Implications for AI Development

Strict evaluation on real complex tasks is needed; current LLMs still have significant gaps in professional fields.

Industry Application Guidance

• Adopt human-machine collaboration mode (AI as an assistant rather than a replacement);
• Establish strict manual review processes;
• Deploy LLMs for subtasks (such as data collection and preliminary analysis) in a targeted manner.

Contribution to Evaluation Research

Its methodology (in-depth expert collaboration, long-running tasks, human benchmarks) can be extended to other knowledge-intensive industries.

Future Outlook: Expansion Plans for FrontierFinance

The team plans to continuously expand the benchmark:

1. Expand the task library (e.g., ESG analysis, cryptocurrency valuation);
2. Support cross-language financial environment evaluation;
3. Introduce real-time market data tasks;
4. Add multi-modal tasks (chart and financial report image analysis).