The number of AI tools available to finance teams has grown faster than the ability of most teams to evaluate them. Every tool promises improved speed, accuracy, and efficiency. But in a finance context, promises are not enough. The consequences of choosing the wrong tool are real: inaccurate data, failed workflows, and time spent correcting automation errors. LLM benchmarking gives finance teams a structured, evidence-based way to make these technology choices.
The Problem With Relying on General Performance Claims
AI tool vendors publish benchmark results, but these are almost always measured on general tasks rather than the specific tasks that matter in a finance workflow. A model that performs excellently on a general reading comprehension benchmark may perform poorly on extracting figures from a complex invoice format or categorising transactions from a bank statement.
General benchmarks tell you how a tool performs in general. Finance teams need to know how a tool performs on their specific work.
What Finance-Specific Benchmarking Involves
Finance-specific benchmarking starts with defining the exact tasks the AI tool will need to perform in the workflow. Invoice extraction. Transaction categorisation. Financial document summarisation. Report commentary generation. Each of these has different accuracy requirements and different error tolerances.
AI workflows built on properly benchmarked tools start with this task definition, then test candidate tools against representative samples of the actual work, and select based on measured performance rather than vendor claims.
Avi Santoso Pty Ltd designs this evaluation process for each finance team based on their specific workflow requirements, ensuring that the tools chosen are genuinely suited to the work they will perform.
Accuracy Requirements Vary by Task
One important insight from finance-specific benchmarking is that accuracy requirements vary significantly across different tasks within the same workflow.
An AI tool extracting numerical figures from invoices needs to be extremely accurate because errors in financial figures have immediate downstream consequences. A tool generating summary commentary for a management report may be able to tolerate a slightly lower accuracy threshold because a human reviewer will check the narrative before it is published.
Understanding these differing requirements, and benchmarking tools against the right standards for each task, is what leads to well-designed AI workflows that perform reliably in the real world.
Accuracy thresholds finance teams typically consider:
- Numerical extraction from invoices: very high accuracy required
- Transaction categorisation: high accuracy with exception flagging
- Document classification: high accuracy for routing decisions
- Narrative commentary: human review before client delivery
- Anomaly detection: precision vs recall trade-off based on consequence
The Role of Ongoing Benchmarking
AI models are updated regularly. New versions sometimes perform better, but not always on every task. Tools that benchmarked well at the time of implementation may underperform after an update. Building ongoing benchmarking into the workflow governance process ensures that performance changes are detected and addressed promptly.
Conclusion
LLM benchmarking is the tool that separates finance teams making evidence-based AI decisions from those making marketing-based ones. For Australian finance businesses building AI workflows, investing in proper evaluation before deployment is the single most effective way to ensure those workflows actually deliver the performance they were built to achieve.
