Tool Evaluation Framework
The F.A.I.R. Framework for Evaluating AI Tools.
F.A.I.R. stands for Fit, Accuracy, Integration and Risk: a structured framework for evaluating AI tools in finance, applying the same rigour finance brings to a capital allocation decision. It was developed by Prime AI Solutions to replace gut-feel tool choices with a repeatable score.
Fit
Default weight 30%Does the tool solve the specific problem you have, for the specific users who will use it? The most fundamental dimension: a technically impressive tool that does not fit the task is worthless.
Accuracy
Default weight 30%How reliable and verifiable are its outputs for finance-grade work? What is your review and sign-off step for what it produces?
Integration
Default weight 20%How well does it connect with your existing ERP, data infrastructure and workflows? Integration gaps are the most common reason finance AI rollouts fail.
Risk
Default weight 20%Data security, compliance, vendor stability and governance. Where is your data processed, and can your inputs train public models?
Scoring
Score each dimension out of 10, multiply by its weight, and compare tools per use case rather than in the abstract. Adjust the weights to your context: a regulated financial-services firm might weight Risk at 40% and drop Integration accordingly; an early-stage company might push Fit and Accuracy higher. Free tools typically score lower on Integration and Risk, so factor in the staff time spent working around their limits, not just the licence fee.
For the full scoring template and worked evaluations of real tools, see our complete guide to the F.A.I.R. framework, or explore our AI for finance training.