1. Scope and Applicability
Neftaly expects all organizations using AI tools to generate or assist in the preparation of budgets to ensure that such budgets remain fully auditable. This applies to corporate, public sector, and non-profit entities where AI-driven budgeting tools influence financial decision-making or reporting.
2. Transparency and Documentation
- Model Documentation: Organizations must maintain comprehensive documentation of the AI model(s) used, including purpose, methodology, input data sources, assumptions, and limitations.
- Algorithmic Decision Rationale: There must be a clear record of how the AI generated budget figures, including intermediate calculations, weighting, and adjustment mechanisms.
- Version Control: Any changes to AI models or parameters that affect budget outcomes must be logged and time-stamped to preserve historical audit trails.
3. Data Governance and Integrity
- Input Data Validation: Entities must ensure that data feeding AI models is accurate, complete, and relevant. Mechanisms should exist to detect and correct erroneous or biased data inputs.
- Data Lineage: There must be a clear mapping from input data to budget outputs, allowing auditors to trace figures back to their source.
4. Audit Trails and Explainability
- Comprehensive Audit Trails: AI-generated budgets must include automated logs of all model runs, user interactions, assumptions applied, and any overrides.
- Explainable Outputs: Budget outputs must be interpretable by human reviewers, with AI-generated recommendations or projections accompanied by explanatory notes to facilitate auditing.
- Simulation and Stress Testing Records: Organizations should maintain evidence of scenario testing and sensitivity analyses performed by the AI, demonstrating the robustness and reliability of generated budgets.
5. Independent Verification
- Third-Party Assessment: Where AI tools have material impact on budget decisions, independent audit or assurance providers should validate AI methodologies, inputs, and outputs.
- Internal Controls: Companies must implement control frameworks ensuring that human oversight exists over AI-generated figures, including approval processes for final budgets.
6. Regulatory Reporting and Compliance
- Organizations must ensure that AI-generated budgets adhere to all applicable financial reporting standards and regulatory requirements.
- Any limitations, assumptions, or uncertainties associated with AI-generated budgets must be disclosed in internal and external reporting.
7. Risk Management and Governance
- Bias and Error Mitigation: Organizations must monitor AI systems for potential bias, anomalies, or errors that could materially affect budgets.
- Governance Oversight: Senior management and audit committees must oversee AI adoption in budgeting, ensuring accountability and alignment with organizational risk appetite.
8. Continuous Improvement and Monitoring
- AI models should be periodically reviewed and recalibrated to reflect evolving organizational, economic, or regulatory contexts.
- Organizations must document updates and retain historical records to support retrospective audits of AI-generated budgets.