AI in Accounting: A Guide to Implementation

AI in Accounting: Tips, Solutions, and Best Practices

Accounting is tedious. Whether you’re a startup founder working solo or a member of a large finance team, it’s a tough, time-consuming process that gets stressful at the end of the month. Fortunately, AI can help solve that problem by fully automating routine tasks and helping with the more complex ones.

A Stanford study found that AI accounting software can help teams close their monthly books 7.5 days faster than those who use traditional methods. In this guide, we’ll explain how that’s possible, how the technology works, the main ways you should use it, and some of the risks you should watch out for as you implement AI. If you’re looking for an all-in-one system that can offer these benefits, Slash may be your answer.¹ Slash is a financial platform that uses AI technology to categorize transactions, scan and parse documents, monitor expenses for fraud, and a lot more.

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Key Takeaways

There are quite a few tasks within an accountant’s typical workload that can be fully automated, including data entry.
You may be able to largely shift your role to reviewer, with routine documents following a predefined workflow and uncertain entries routed to an exception queue for extra attention.
AI shouldn't replace payment controls. Segregation of duties, approval thresholds, independent bank-detail verification, and audit logs all still apply, and models shouldn’t approve a new vendor or release a large transfer just because the activity looks normal.
AI doesn't necessarily make forecasts more accurate, but it can make them faster, producing and refreshing scenarios in minutes.
It’s easy to miss errors that happen inside fully automated steps. A human mistake usually gets caught on review, while an AI mistake in a human-free process can pass by undetected.

How Is AI Used in Accounting?

AI in accounting isn’t one feature or piece of technology. It’s a collection of tools that can recognize patterns, interpret documents, generate language, and automate parts of a clear workflow.

Older systems used to rely heavily on rules and something called robotic process automation. A rule might post the same software charge to one account each month, while robotic process automation could copy information between systems. Optical character recognition (OCR) was developed to scan invoices and receipts, making them machine-readable. Machine learning models then added the ability to learn from historical transactions, improve categorization, detect anomalies, and estimate likely outcomes.

Generative AI adds a new element: conversational language. Large language models (LLMs) can summarize contracts, draft policies, explain a report, and even forecast finances into the future. They can break free of “if/then” logic and use their own sort of complex judgment to execute tasks.

You’re probably familiar with some of the independent, general-purpose AI models teams can utilize. For example, an accountant might use Claude to summarize a policy or create a close checklist. Other types of AI are embedded directly into financial software. An accounts payable platform may extract invoice data, while a close management system drafts analyses using information connected to the ERP. Slash’s AI technology can extend these abilities further by digging through your financial data and surfacing anomalies that need human attention.

Overall, embedded tools are often narrower in scope, but they can be easier to govern because their permissions and data sources are more clearly defined.

Benefits of AI in Accounting

AI tools can perform work that busy teams don’t have the bandwidth to fully focus on. Here’s how that can help your accounting crew:

Increased efficiency and accuracy: AI can do things like categorize transactions, extract invoice fields, match receipts, and compare balances faster than manual entry. You’ll also be able to avoid typos and overlooked errors that can happen when burnt-out accounting professionals are cramming at the end of the day.
Cost savings and time management: When you automate invoice processing, expense tracking, reconciliations, and first-draft reporting, you’ll save a whole lot of time. In a sense, this also saves money; accounting teams can redirect their effort towards cash management and planning that could have otherwise required an extra hire.
Improved decision-making: AI can scan large datasets for patterns and present the findings in a usable form, like a chart or writeup. This type of quick analysis can help teams identify unhealthy margins, unusual expenses, late-paying customers, or gaps between forecasts and actual results. However, you’ll still need a human accountant to take action on those patterns, of course.

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Implementing AI in Accounting: Key Use Cases

We’ve talked a little about how AI can automate AP and invoice processing, but they’re broad subjects that have a lot of moving parts. Let’s dive a little deeper into the ways accounting teams can take advantage of AI:

Faster Data Entry

Since data entry is typically repetitive, and the information isn’t particularly complex, it can be a great place for AI to work. OCR can read a receipt, bank statement, or invoice, while machine learning identifies fields such as the vendor, date, amount, currency, and tax. The system can then suggest a general-ledger category or match the document with an existing transaction.

That means you and your team can go from typists to reviewers. You have the power to adjust anything that needs an extra look, while most other documents and tasks can follow a predefined workflow. Some AI systems may also learn from corrections, although teams should stay on top of those learned patterns as vendors and accounting policies change.

You can get the most out of these sorts of tools when they can extract information directly to the ledger or expense platform. Slash, for instance, syncs two-ways with accounting apps like QuickBooks Online, Sage Intacct, NetSuite, and Xero.

Streamlining Accounts Payable

Accounts payable often combines document handling, coding, approvals, payment scheduling, and reconciliation. Unsurprisingly, AI can speed each of these things up. Many AI tools can extract invoice details, recognize a known supplier, recommend a cost center, and flag missing or inconsistent information within bills or documents.

Importantly, it can also prioritize exceptions. Things like unexpected changes in bank details or mismatches with typical vendor behavior can be sent for review, while routine bills are treated normally.

While these abilities are all helpful, they can only go so far. AI doesn’t remove the need for payment controls. A model shouldn’t approve a new vendor or release a large transfer merely because the activity looks normal. Segregation of duties, approval thresholds, independent bank-detail verification, and audit logs still remain necessary, even with tools that are supposed to make it all simpler. With Slash, you can set payment policies that restrict transfers above X amount from executing without admin approval.

Fraud Detection

Traditional fraud rules search for known warning signs, such as a payment above a set threshold. Machine learning can compare a transaction with a broader history and flag behavior that seems unusual for the vendor or business. That may include a new payment destination, repeated identical invoices, spending at an unexpected time, or a sudden change in frequency. The system can score the activity and point out high-risk items.

An anomaly isn’t proof of fraud in and of itself, however. A new office, acquisition, or emergency purchase can look unusual to an AI tool that isn’t in the loop. On the other hand, models can also overlook misconduct that resembles normal behavior. Finance teams should use AI to improve detection, but not to replace security measures entirely.

Financial Forecasting

Forecasting tools can use historical revenue, expenses, cash flows, and operating drivers to identify trends and estimate future performance. AI can update projections as new data arrives, compare actual results with the plan, and generate explanations for those forecasts.

These tools may also make analysis easier to understand overall. With a conversational model, a finance leader may be able to ask which customers caused receivables to rise or which expense categories are growing unusually quickly, then receive a summarized answer that links to the data.

It’s important to note that an AI can’t necessarily create more accurate forecasts, but it can certainly generate them more quickly. AI can produce and refresh scenarios within minutes, while you (the expert) can decide which assumptions are credible based on the context a tool couldn’t possibly know.

Challenges of Implementing AI in Accounting

When it comes to introducing AI to an environment filled with sensitive financial data, there’s a lot to be wary of. Mistakes that would normally be quickly spotted by a human might go unnoticed if the workflow is too “hands-off”. Here are some things to watch out for as you consider adding AI to your day-to-day accounting processes:

Inconsistent application: AI often appears within individual steps rather than across a full workflow. One tool might read an invoice while another still requires manual coding and reconciliation. These partial implementations can end up creating new handoffs and conflicting records.
Data security concerns: Your accounting system contains your bank details, payroll information, customer records, and contracts. Sending that data to an AI model without understanding training, access, and hosting policies can create privacy and compliance issues. Some financial platforms, such as Slash, have added data retention rules and role-based permissions to their AI tools meant to help keep sensitive information more secure.
Missed inaccuracies: AI can classify a transaction incorrectly, invent an explanation, overlook an exception, or hallucinate something that isn’t there. When humans make these sorts of mistakes, they’re often spotted during a review or a quick double-check. When AI makes mistakes, it’s often in a process that’s supposed to be human-free, meaning there’s no one to give the results a final look-through.
Integration with existing systems: AI performs best with clean, consistent data. Old software, custom spreadsheets, and inconsistent charts of accounts can lead to complex calculations being performed with numbers a human never would have used. You’ll need to keep your surrounding systems up-to-date in order to make sure your AI is working with the correct data.

Optimize Your Accounting Processes With Slash

The question of “how” to implement AI is a lot tougher to answer than the question of “why”. Should you seek help with a general-purpose AI model like ChatGPT, or should you invest in an expensive enterprise-level software that automates AP and AR? You don’t have to do either. You can choose Slash.

Slash is an all-in-one banking platform that comes with a business checking account, a corporate card program, AP/AR tools, accounting integrations, and AI tools built to help you manage it all together. As your team spends money, whether with a Slash Visa® Platinum Card or through other rails, each transaction is automatically coded by AI and categorized according to rules you predefine. If any of those expenses are deemed suspicious or high-risk, our system can automatically flag them and surface them for review.

You can also use our platform’s AI tools to break down your company spending and get a better look at trends that have developed over time. Evaluating expenses at this level used to require an afternoon of sifting through paperwork and using a calculator, with human mistakes nearly guaranteed. Let Slash’s AI handle it instead.

Busy finance teams can also take advantage of the following Slash features:

Reimbursements: Instead of managing reimbursements across multiple tools, teams can submit, review, and approve reimbursements directly inside the Slash dashboard. Connect your bank account, upload your receipt, and let Slash capture the details.
Separate virtual accounts: Create multiple business bank accounts to silo cash flows by project, department, or client with real-time analytics across each of them.
Diverse payment rails: Slash supports a wide range of payment methods, including card spend, global ACH, international wire transfers to over 180 countries via SWIFT, and real-time domestic payments through RTP and FedNow.
Accounting & ERP integrations: Sync transaction data with QuickBooks Online, Xero, NetSuite, or Sage Intacct to streamline reconciliation, reporting, and month-end close.
Business banking: FDIC-insured business checking, protected up to $150M through Column N.A.'s insured cash sweep network.²

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Frequently Asked Questions

How does using AI affect an audit?

Auditors care about your control environment, and they want to know who reviewed what and whether exceptions were actually investigated. That means you should keep audit logs, evidence of human sign-off at approval points, and documentation of how your categorization rules were set and changed.

Can AI automate accounts receivable?

Definitely. AI can match incoming payments to open invoices, score which customers are likely to pay late, and draft outreaches itself if you’re using a tool like ChatGPT. That said, decisions to escalate, extend terms, or write off a balance should stay with a person.

Will AI replace accountants?

Probably not, even in the future. AI replaces tasks rather than roles; judgment calls on things like revenue recognition, capitalization, and tax positions should still require an expert who can approach them with extra context.