The CFO's guide to AI-ready data: Ensuring accuracy and control

AI's promise in finance is compelling – faster closes, automated reporting, and insights that drive better decisions. But there's a fundamental truth most CFOs discover the hard way: AI is only as good as the data you feed it. Get the data wrong, and you'll get unreliable outputs, frustrated teams, and potentially costly mistakes.

This guide shows you how to prepare your finance data for AI success, avoid the infamous "hallucinations", and build confidence in AI-driven workflows.

Why data quality matters more than ever

Finance teams deal with a company's most sensitive and critical information. Unlike marketing campaigns, where a minor error might go unnoticed, finance mistakes compound quickly – affecting compliance, investor relations, and strategic decisions.

"AI, to work well, needs clean, accessible and organised data," explains Anne-Claire Chanvin, founder of Finup360. "Conduct a data audit to assess the quality of your data."

The stakes are high: poor data quality leads to AI outputs you can't trust, which defeats the entire purpose of automation.

Understanding AI hallucinations in finance

AI "hallucinations" occur when models generate plausible-sounding but incorrect information. While this gets significant attention in the media, the reality for finance teams is more nuanced.

"The models are pretty good if you provide the full data set and ask it for something like a summary," says Paul Jun of Depth Capital. "Where you run into problems is if you ask a question that's not in the data set. Then it will create an answer, and the answer will be just wrong."

The good news? Most finance work involves a company's own data, which dramatically reduces hallucination risk. The key is ensuring your internal data is accurate and complete.

When hallucinations matter less

Sarah Fu from Elsa Capital points out that context matters: "You can feed the AI tools many different industry reports, competitive reports, and internal documents to synthesise or digest all that information. In this case, the cost of hallucination is very low, since you're just trying to gather some basic information."

Low-risk use cases for AI include:

• Summarising multiple reports or documents

• Extracting key themes from board meeting notes

• Synthesising market research for strategic planning

• Drafting initial commentary for financial reports

High-risk areas require more caution:

• Generating specific financial figures

• Making regulatory compliance determinations

• Creating audit trail documentation

The data audit: Your starting point

Before implementing any AI solution, conduct a comprehensive data assessment:

Map your data landscape

• Where does financial data live? (ERP, CRM, spreadsheets, contracts)

• How is data currently formatted and structured?

• What's the quality and completeness of historical data?

• Who owns and maintains each data source?

Identify data quality issues

• Inconsistent naming conventions (vendors, chart of accounts, cost centres)

• Duplicate records and missing information

• Manual processes that introduce errors

• Data silos that prevent comprehensive analysis

Assess integration readiness

• Can systems talk to each other?

• Are there clear data governance policies?

• Do you have proper access controls and audit trails?

The silver lining: AI can help clean your data

One of the most practical applications of AI is data cleaning itself. "AI tools are faster – and often better – than humans at reformatting and validating large swathes of data," notes the report.

Common data cleaning tasks AI handles well:

• Standardising vendor names and addresses

• Reformatting dates and currency fields

• Identifying and flagging duplicates

• Filling in missing information from context

• Converting between different file formats

Start here if you have messy historical data – use AI to get to "good enough" quality before implementing more complex workflows.

Building data structures that prevent problems

Paul Jun emphasises prevention: "Setting up your data structures in the right way from day one is immensely helpful and prevents the problem – kind of like technical debt – from creeping up in the first place. An ounce of prevention is worth a pound of cure."

Best practices for AI-ready data:

Standardisation first

• Consistent chart of accounts across entities

• Standardised vendor master data

• Uniform cost centre and department codes

• Clear data definitions everyone understands

Single source of truth

• Designate authoritative systems for each data type

• Establish clear data ownership and maintenance responsibilities

• Implement regular data quality monitoring

• Create clear escalation paths for data issues

Access and security controls

• Role-based permissions for sensitive financial data

• Clear policies on what data can be used with external AI tools

• Audit trails for data access and modifications

• Compliance with regional data protection requirements

Practical implementation: Learning from real examples

OpenAI's approach: When their finance team faced Excel limitations with large datasets, they used ChatGPT to generate Python scripts for data processing. The key was validating outputs against historical manual calculations to ensure accuracy.

LendingTech's method: Gabriela Suchanek's team implemented strict security protocols: "When dealing with AI, we never send data directly to AI models – instead, we generate queries locally and use tokenisation for sensitive information." They also implemented a "double-eye principle" for validation.

Everphone's governance: Veronika von Heise-Rotenburg built comprehensive controls: "We're developing a granular permission system that ensures users access only variables and data sources they're allowed to. The AI model also asks you what you want to achieve with the request, and if it thinks the request might be used wrongly, it asks the user's manager for permission."

Security and governance essentials

Data confidentiality guidelines

• Clear policies on what data can be shared with external AI tools

• Review the terms and conditions of any AI platforms carefully

• Prefer paid plans that contractually prohibit data sharing or training

• Consider on-premise or private cloud solutions for sensitive data

Validation frameworks

• Build "double-eye" validation into automated processes

• Compare AI outputs against manual calculations during pilot phases

• Implement exception reporting for unusual results

• Maintain human approval checkpoints for critical processes

Audit and compliance

• Log all AI interactions and outputs for regulatory purposes

• Maintain clear documentation of AI decision-making processes

• Regular review cycles to ensure continued accuracy

• Clear escalation procedures for data quality issues

Getting started: A practical checklist

Week 1-2: Assessment

• Map current data sources and quality

• Identify biggest pain points and inconsistencies

• Assess team skills and tool requirements

• Define success metrics for data quality improvement

Week 3-4: Quick wins

• Use AI to clean and standardise one key dataset

• Implement basic validation rules and checks

• Establish data governance policies

• Train team on new data standards

Week 5-6: Pilot implementation

• Choose one low-risk use case for AI implementation

• Build validation processes and exception handling

• Test outputs thoroughly against known good data

• Document lessons learned and refine processes

Ongoing: monitoring and improvement

• Regular data quality audits and metrics review

• Continuous training on data governance best practices

• Stay current with AI tool capabilities and security features

• Expand to additional use cases based on pilot success

The payoff: Confidence in AI-driven finance

When you get data quality right, AI becomes a powerful ally rather than a source of anxiety. Teams report dramatic improvements in accuracy, speed, and confidence in their outputs.

The investment in data quality pays dividends beyond AI implementation – better data improves every aspect of financial operations, from routine reporting to strategic analysis.

Start with your data foundation, build robust governance, and you'll be positioned to leverage AI's full potential while maintaining the accuracy and control finance teams require.