AI in Finance: Where It Can Help Finance Teams

Finance teams handle information from many sources: invoices, payment advice, journals, account balances, forecasts and supporting documents. AI can help process some of this information, analyse patterns in it or produce predictions from it.

Those are different tasks, with different risks and requirements. Extracting data from an invoice is not the same as deciding whether a payment should be posted. A forecast can inform planning, but it doesn’t confirm that a balance is correct.

So where can AI help finance teams? A useful starting point is to look at the work itself, identify the point that takes the most time or judgement, then decide what a good result would look like.

SAP’s overview of AI use cases in finance groups examples across document and transaction processing, analytics, predictions, detection and user interaction.

Where AI may support finance work

These examples describe potential applications. The capabilities available will depend on the system, data and process involved. SAP’s Document AI overview explains how AI can extract information from business documents.

Accounts payable and document processing

Accounts payable teams may receive invoices and supporting documents in different formats. AI document processing can help extract information such as supplier details, dates and amounts, so it can be used in a wider invoice workflow.

That doesn’t mean the extracted information is automatically ready to post. The team still needs to know what checks happen before it reaches the next step, how unclear details are handled and who can resolve an exception.

Teams should also distinguish between document processing and other AP tasks. Capturing information from an invoice is different from checking it against a purchase order, approving it or scheduling payment. A proposed AI use case should specify which part of that workflow it supports.

Accounts receivable and payment information

Accounts receivable processes may involve payment advice, remittance details and open invoice data. AI may help interpret incoming information or support the analysis of transaction data. A separate matching process can then compare payment information with open items.

Here, it’s useful to ask whether the main challenge is receiving and interpreting information, comparing transactions, or investigating items that don’t match. Each points to a different process need. Teams should be clear about which system performs each task and how unmatched items are reviewed.

Financial close and account analysis

During close, teams may need to review balances, investigate differences, prepare supporting information and track approvals. Analytics or detection tools may help draw attention to patterns or items for investigation, while workflow automation can help manage review steps.

A flagged item still needs context. The finance team should be able to see which data was considered, why it was raised for review and how the issue was resolved. For teams reviewing SAP close activities, this overview of the SAP financial close process explains how reconciliations, clearing and approvals fit into period-end work.

Planning, forecasting and finance analysis

AI may also support finance teams that analyse historical and current data to estimate future outcomes. Examples include forecasting cash flow, estimating late-payment risk or identifying trends that may affect planning.

These outputs can inform decisions, but they’re estimates based on available data and assumptions. Teams should understand which information informs a prediction, how its accuracy is assessed and when it needs updating. A forecast should be reviewed alongside the business context and other relevant evidence.

Choose a use case with a clear purpose

A useful AI use case starts with a defined task. ‘Use AI in finance’ is too broad to assess on its own. Teams can make the task clearer by working through a few questions:

1. Where does the work slow down? Identify the step that takes time, creates rework or delays the next activity.
2. What kind of task is it? Separate document processing, transaction comparison, analysis, prediction and user support.
3. What information is available? Check its source, format, completeness and sensitivity.
4. What should the output do? Be specific about whether it should extract information, flag a pattern, produce an estimate or help a user find information.
5. Who checks the result? Assign responsibility for validating outputs and handling exceptions.

This also helps teams decide whether AI is suitable. A task based on fixed, predictable conditions may be handled well by existing rules or standard automation. AI may be more relevant where the work involves interpreting documents, finding patterns or estimating outcomes. The choice depends on the task and how much uncertainty the finance process can manage.

Measure the result

Before a pilot, record how the process works today. The baseline gives the team something to compare against once the proposed change is in use.

Depending on the task, useful measures might include:

• Time spent processing each document or transaction
• The percentage of work needing manual correction
• The volume of items sent for review
• The number of unresolved exceptions
• Forecast accuracy over a defined period
• Delays between receiving information and completing the finance task

Measures should reflect the intended outcome. If the aim is to reduce document handling, measure time and correction rates. If the aim is to improve analysis, check whether the outputs help staff investigate or decide sooner. A single measure rarely shows whether a change has improved the whole process.

Reconciliation as one example

Reconciliation is one area where AI may support a wider finance process. It can help with specific input tasks, while matching, review and approval remain part of the organisation’s defined workflow.

BEST currently uses AI for document reading in the BEST Vendor Recons module and the BEST Customer Clearing module, as well as date cracking in classic BEST solutions. The relevant BEST process handles matching and clearing. For a more detailed explanation of this specific area, see the role of AI in SAP reconciliation automation.

The BEST Vendor Recons module supports supplier statement reconciliation within SAP. Teams can assess how that process fits their requirements and where document reading may support the work. For more on reconciliation reporting and review, see features for audit trails and balance sheet reporting.

A practical approach to AI in finance

AI can support different finance tasks, but a good use case starts with the work rather than the technology. Define the task, identify the information it needs, decide how the output will be used and agree how staff will review it.

With a clear purpose and suitable measures, finance teams can assess where AI may help, where other forms of automation are appropriate and what needs to be in place to use the output responsibly.

Frequently asked questions about AI in finance

What are common uses of AI in finance?

Potential uses include processing business documents, comparing transaction data, analysing financial information, identifying patterns, making predictions and helping users find or summarise information.

How can finance teams choose an AI use case?

Start with a specific task the team wants to improve. Check what information it needs, what the output should do, how staff will validate it and what success will look like.

Can AI make finance decisions on its own?

AI can produce information, analysis or predictions to support a process. Finance teams should define how those outputs are checked and who is accountable for decisions.

How does BEST use AI today?

BEST uses AI for document reading in the BEST Vendor Recons module and the BEST Customer Clearing module, and for date cracking in classic BEST solutions. These capabilities support specific tasks within wider SAP finance processes.

Book a demo to see how BEST supports reconciliation and clearing processes in SAP.