How to Use AI for Finance Teams: A Practical Guide

AI can help finance teams process information, identify patterns, summarize documents, detect unusual transactions, support forecasting, and automate parts of recurring workflows. Generative AI can also help professionals work with natural language, making it easier to interact with financial information and create first drafts of reports or explanations.

However, finance is also a high-responsibility environment. An incorrect financial output can affect business decisions, reporting, compliance, and customer relationships. AI therefore needs to be introduced carefully, with appropriate validation, security, governance, and human oversight.

The NIST AI Risk Management Framework recommends managing AI through four interconnected functions: Govern, Map, Measure, and Manage. This provides a useful foundation for organizations adopting AI responsibly.

What Does AI Mean for Finance Teams?

AI for finance is not simply about asking a chatbot to perform accounting tasks.

It involves combining AI technologies with existing financial systems and workflows to improve how information is processed and decisions are supported.

Depending on the use case, finance teams may use:

• Generative AI

• Machine learning

• Predictive analytics

• Natural language processing

• Intelligent document processing

• AI agents

• Workflow automation

• Data analytics

For example, an invoice-processing workflow could use AI to extract information from an invoice, compare it with a purchase order, identify discrepancies, and route exceptions to the appropriate employee.

The important distinction is that AI can assist with the processing and analysis, while finance professionals retain responsibility for important judgments and approvals.

Why Should Finance Teams Use AI?

Finance departments are well suited to AI because they work with structured data, documents, recurring processes, and analytical tasks.

AI can potentially help teams:

• Reduce manual data entry

• Process documents faster

• Identify anomalies

• Improve forecasting

• Automate routine reporting

• Summarize financial information

• Support financial planning

• Improve access to internal knowledge

• Reduce repetitive administrative work

The opportunity is not necessarily to reduce the importance of finance professionals. Instead, AI can shift their time toward activities that require business understanding, critical thinking, communication, and strategic decision-making.

PwC identifies finance areas such as invoice processing, purchase-order matching, collections, financial close, supplier risk monitoring, and liquidity optimization as potential areas for AI-agent-enabled workflows.

1. Use AI for Financial Reporting

Financial reporting often requires information to be collected from several sources before it can be reviewed and presented.

AI can assist with tasks such as:

• Summarizing financial results

• Identifying significant changes

• Preparing draft management commentary

• Comparing reporting periods

• Organizing supporting information

• Extracting information from financial documents

For example, an AI system could compare monthly results and highlight significant movements in revenue or operating expenses.

A finance analyst can then investigate the reasons behind those movements and validate the AI-generated summary.

This creates a useful division of responsibility:

AI identifies and organizes information. Finance professionals interpret and validate it.

AI-generated financial content should not automatically be treated as authoritative. Important financial outputs require appropriate human review.

2. Improve Financial Forecasting

Forecasting is another area where AI can support finance teams.

Traditional forecasting may rely heavily on spreadsheets, historical trends, management assumptions, and periodic updates.

AI and predictive models can analyze larger datasets and identify patterns that may be difficult to detect manually.

Potential applications include:

• Revenue forecasting

• Expense forecasting

• Cash-flow forecasting

• Demand forecasting

• Working-capital analysis

• Scenario analysis

For example, finance teams can compare different business scenarios and examine their potential impact on revenue, expenses, cash requirements, or investment decisions.

The value of AI is not that it can predict the future perfectly. Rather, it can help finance professionals analyze information more quickly and evaluate multiple possibilities.

3. Automate Invoice Processing

Invoice processing is one of the practical areas where AI and automation can work together.

A finance workflow can potentially:

1. Receive an invoice.

2. Extract relevant information.

3. Identify the supplier.

4. Compare the invoice against purchase-order information.

5. Identify discrepancies.

6. Route exceptions to a finance employee.

7. Send approved information to the accounting system.

This reduces repetitive manual work while maintaining human involvement where exceptions require investigation.

PwC describes invoice processing and purchase-order matching as examples of finance activities where AI agents can take on repetitive work and support finance professionals.

4. Use AI for Account Reconciliation

Reconciliation involves comparing information from different sources and identifying differences.

AI-assisted reconciliation can help identify:

• Matching transactions

• Missing transactions

• Duplicate entries

• Unexpected differences

• Unusual patterns

Instead of manually reviewing every transaction, finance professionals can focus on exceptions.

This can be particularly useful when transaction volumes become too large for efficient manual review.

However, reconciliation rules, data quality, and validation processes still matter. AI should complement established financial controls rather than bypass them.

5. Improve Expense Management

Finance teams can also use AI to support employee expense processes.

AI can help:

• Read receipts

• Extract transaction details

• Categorize expenses

• Identify missing information

• Compare expenses against policies

• Flag unusual transactions

For example, instead of manually entering information from every receipt, an intelligent document-processing system can extract the relevant details and prepare them for review.

The finance team can then concentrate on exceptions and policy-related issues.

6. Support Fraud and Anomaly Detection

Fraud detection is another important AI application.

Machine learning systems can analyze transaction patterns and identify activities that appear unusual.

Potential signals could include:

• Unexpected transaction amounts

• Duplicate payments

• Unusual transaction timing

• Abnormal vendor activity

• Changes in spending patterns

However, an anomaly is not automatically fraud.

A legitimate transaction can look unusual for many reasons. Therefore, AI should generally be used to prioritize transactions for investigation, rather than independently declaring that fraud has occurred.

This approach reduces the possibility of over-relying on automated conclusions.

7. Support Accounts Receivable

AI can help accounts receivable teams manage outstanding payments more efficiently.

Potential applications include:

• Prioritizing overdue accounts

• Identifying payment patterns

• Summarizing customer histories

• Drafting collection emails

• Predicting potential payment delays

• Organizing follow-up activities

AI agents are increasingly being explored for finance processes including collections and order-to-cash operations.

Finance professionals can then focus on customer relationships, complex cases, and decisions that require negotiation or judgment.

8. Use AI for FP&A

Financial Planning and Analysis (FP&A) teams spend significant time collecting data, preparing forecasts, analyzing variances, and communicating financial insights.

AI can assist with:

• Budget analysis

• Variance analysis

• Forecast preparation

• Scenario modeling

• Management reporting

• Financial summaries

Imagine a manager asking:

"What were the main drivers behind this quarter's expense increase?"

Instead of manually reviewing multiple reports, an AI-enabled system could identify relevant movements and prepare an initial explanation.

The FP&A professional would then verify the numbers, investigate the underlying business causes, and refine the analysis.

This allows AI to act as a financial analysis assistant rather than a replacement for financial expertise.

9. Create an AI Finance Knowledge Assistant

Finance departments often maintain large collections of policies, procedures, reports, accounting documents, and internal guidance.

Finding a specific piece of information can take time.

An AI knowledge assistant can help employees ask questions in natural language.

For example:

"What is our expense approval process?"

or:

"Which policy applies to this type of purchase?"

A properly configured system can retrieve information from approved internal sources and provide an answer.

This approach can make financial knowledge more accessible while reducing repetitive questions directed toward finance staff.

Start With a Specific Problem

One of the biggest mistakes organizations can make is starting with the technology instead of the business problem.

Rather than asking:

"How can we use AI?"

ask:

"Which finance process is consuming unnecessary time or creating avoidable problems?"

Potential starting points include:

• Invoice processing

• Reporting summaries

• Reconciliation

• Employee expense processing

• Internal finance questions

• Forecast preparation

Select a process where the expected benefit can be measured.

Assess Your Data Before Automating

AI depends heavily on the quality and accessibility of information.

Before implementation, finance teams should examine:

• Data accuracy

• Data completeness

• Data consistency

• Data accessibility

• Data ownership

• Security requirements

• Retention requirements

If financial information is spread across disconnected spreadsheets and outdated systems, implementing AI without addressing the underlying data problems may produce disappointing results.

Good AI adoption starts with good information management.

Build a Controlled Pilot

A finance department does not need to automate everything simultaneously.

Start with a small pilot.

For example:

Pilot: Invoice Classification

Input: Supplier invoice

AI: Extracts and categorizes information

Automation: Routes the invoice

Human: Reviews exceptions

Output: Approved information moves into the financial workflow

After the pilot, evaluate:

• Processing time

• Accuracy

• Exception rate

• Employee adoption

• Cost savings

• Quality of AI output

If the results are positive, expand gradually.

Establish AI Governance

Finance teams should treat AI governance as part of their broader control environment.

NIST recommends that organizations establish governance across the AI lifecycle and clearly define roles, responsibilities, policies, monitoring, and risk-management practices.

A finance AI governance framework should address:

Data Governance

Define what financial information can be used with AI systems.

Access Control

Ensure only authorized users can access financial information.

Human Oversight

Define which outputs require human review.

Auditability

Maintain appropriate records of important AI-supported processes.

Vendor Management

Evaluate third-party AI providers and their data-handling practices.

Monitoring

Regularly evaluate AI performance and identify problems.

NIST's Generative AI Profile also highlights governance, pre-deployment testing, content provenance, and incident disclosure as important considerations for generative AI systems.

Why Human Oversight Matters

Finance is not an area where every decision should be delegated to AI.

Human review is particularly important for:

• Financial reporting

• Significant accounting judgments

• Tax matters

• Regulatory submissions

• Fraud investigations

• Material financial decisions

• Sensitive customer or supplier situations

AI can provide recommendations, summaries, or analysis, but qualified professionals should remain accountable for important financial decisions.

The NIST AI RMF emphasizes clearly defined human-AI roles and oversight as part of responsible AI risk management.

Measure AI's Business Impact

Finance leaders should measure whether AI actually improves the process.

Useful KPIs include:

Processing Time

How long does the task take before and after automation?

Error Rate

Are manual or AI-related errors decreasing?

Exception Rate

How frequently does the system require human intervention?

Reporting Cycle Time

Can reports be prepared faster?

Forecast Accuracy

Does AI-supported forecasting improve forecast performance?

Cost per Transaction

Does automation reduce the cost of processing routine activities?

Employee Productivity

Are finance professionals spending more time on analysis and strategic work?

The right metrics depend on the specific workflow.

Common AI Challenges for Finance Teams

Inaccurate AI Outputs

Generative AI can produce incorrect information. Financial data and AI-generated explanations must therefore be validated before important use.

Data Security

Finance systems contain sensitive information. Organizations should use approved platforms and appropriate security controls.

Poor Data Quality

AI cannot automatically fix fundamentally unreliable financial data.

Integration Complexity

AI workflows may need to connect with ERP, accounting, banking, procurement, and reporting systems.

Employee Adoption

Finance professionals need training to understand both the capabilities and limitations of AI.

Over-Automation

Not every financial process needs AI. In some cases, conventional automation or a simpler software solution may be more reliable.

The Future of AI in Finance

The next stage of AI adoption will likely involve more connected workflows rather than isolated AI tools.

AI agents may coordinate multiple activities across finance applications.

Continuous forecasting may help finance teams respond more quickly to changes in business conditions.

Conversational analytics may allow professionals to interact with financial information using natural language.

Intelligent financial operations may connect AI, automation, analytics, and enterprise systems into increasingly integrated workflows.

PwC notes that AI agents are already being explored across areas including procure-to-pay, order-to-cash, record-to-report, FP&A, treasury, and financial close activities.

The finance professional of the future therefore may spend less time collecting and formatting information and more time interpreting it, challenging assumptions, advising business leaders, and managing risk.

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Conclusion

AI can give finance teams a practical way to reduce repetitive work and improve how financial information is processed and analyzed.

From invoice processing and reconciliation to forecasting, reporting, FP&A, expense management, anomaly detection, and finance knowledge assistants, AI can support a wide range of activities.

But successful AI adoption requires more than selecting a powerful model.

Finance teams need:

• Reliable data

• Clearly defined use cases

• Strong security

• Human oversight

• Appropriate governance

• Measurable objectives

• Continuous monitoring

The strongest approach is to start small, prove value, learn from the implementation, and then scale.

AI should not replace financial expertise. It should give finance professionals better tools to apply that expertise.

As organizations increasingly adopt intelligent automation, professionals who understand AI, data, automation, financial processes, and responsible AI practices will be better prepared for the changing finance function.