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Today, Large Language Models (LLMs) can claim to pass the CPA exam, but are they really ready to take over the accounting industry? In this article, we dive into what actual AI accounting automation looks like (and why it isn’t as straightforward as it seems).

Introduction

There is simply no escaping the fact that AI is the most talked about new technology on the internet in 2024. ChatGPT, the popular chat-based interface for exploring the LLM (Large Language Model) capabilities developed by OpenAI, was released to the public earlier in the year.

Play around with it for just a few minutes, and you can begin to understand why everyone and their dog is talking about this; ChatGPT is able to demonstrate superhuman proficiency in virtually every domain. Artificial intelligence clearly promises to significantly transform many areas of work - while potentially impacting millions of jobs and careers.

Artificial intelligence is now one of the major future trends, and is being applied across professional domains that are ripe for automation - areas of work such as software, law, consulting and financial processes that typically have routine tasks. Within finance, the accounting industry is one that comes into the spotlight as somewhat unique - especially as there seems to be an equal amount of noise on both sides of the argument, with AI advocates and naysayers both having a raging debate on what will (or won’t) happen.

The jury is still out on how exactly this rapid transformation will be achieved - and this is where most discourses on the benefits of ChatGPT in particular (and AI in general) tend to draw the line.

The need for AI in Accounting

In traditional accounting firms, companies often rely on manual processes, extensive paperwork, and repetitive tasks to handle their payables function. These tasks are activities like data entry, invoice processing, and financial analysis, which are crucial for decision-making, operational planning, and risk management.

However, these processes involve spending time (and money). The major drawbacks of manual accounting tasks are:

  1. Manual data entry introduces a high potential for errors, as humans can make mistakes when entering data in high volumes. Think of fields like invoice numbers, dates, dollar amounts - getting any of these wrong has major consequences.
  2. It is time-consuming, requiring long hours of work to reconcile accounts, generate reports, and perform analysis on financial statements.
  3. It is heavy on synchronous communication. Have you encountered situations like the ones below?

a. Approvals don't happen until you get the client and the CPA on a call

b. Line items don't get resolved until the client schedules a meeting with your team who is doing the invoice data entry and document management

All of this leads to delays in monthly close for accountants, clients, late vendor payments, inadequate expense planning, and issues in maintaining financial integrity.

AI for accounting doesn't have to mean a complete overhaul

The problems listed above are well-documented - and when asked, most accounting teams will agree that introducing AI accounting tools will definitely help them out. Technologies such as machine learning and natural language processing have the ability to revolutionize the accounting profession in a very deep way - provided they’re implemented and integrated in the correct manner.

However, this usually leads many to the conclusion that AI-powered tools are not for them - they seem cumbersome, time-consuming and expensive to implement.

The reality, though, could not be more different - today it is possible to get started with using AI for your accounting process withing minutes. And you start automating your routine tasks without compromising on your current process's reliability, security and efficiency.

Put generative AI and LLMs aside for one moment - the reality is that even entry-level AI automation can help significantly in addressing these issues. Even the humble OCR - that has been around for decades - reduces human error and the time taken to process an invoice by at least 60%, saving accounting firms multiple days every month. And yet adoption of this technology is still not widespread.


Looking to integrate AI into your Accounting function? Book a 30-min live demo to see how Nanonets can help your team implement end-to-end accounting automation.

Potential use-cases for AI within the Accounting process

So how exactly are you supposed to integrate AI into your accounting process? Where do you start?

The first place to begin is to look at which part of the process really take up most of the time. Tyical bottlenecks that are reported by accounting teams are activities like:

  1. Invoice coding
  2. General Ledger (GL) mapping
  3. Payment Details Verification (to check for fraud)
  4. Duplicate Detection
  5. Data Analysis and Financial Reporting

There is a very clear underlying theme here - manual data entry and the risk of human error is what causes these tasks to be tedious and time-consuming.

Automation Trends 2022 Report: The Speed of Change

This survey graphic above (from the Automation Trends 2022 report) reveals a lot - almost 70% of accountants have still not automated the most pressing issues in their accounting process. The tasks listed above are all manual - someone needs to look at the actual data on the invoice and confirm that it is correct, before proceeding further.

As such, automating these tasks might feel overwhelming, since you're now trusting a machine to have the same level of discretion as a (trained) human.

The good news? AI technology can be trained equally well too! We go deeper into some use cases of this, below.

1. Invoice coding and General Ledger (GL) account mapping

Perhaps one of the most difficult tasks to automate is assigning invoices and receipts to the right category and GL code within your accounting system. Why is this particularly tricky?

There are often multiple GL codes that apply to the same expense, split by line items/individual product codes. Assignment of these GL codes is usually manual, and must be done in consultation with business teams and the CFO.

Assigning a GL code to an invoice is sometimes subjective - for example, while regular sales invoices might always be assigned to “Sales” in your chart of accounts, sometimes the exact same invoice format ends up being used for contractors and non-employees. This can lead to contractual expenses being incorrectly tagged as “Sales” by basic automation tools.

How can AI help here?

Automated invoice coding based on LLM processing

Automate invoice coding based on LLM processing - here, the AI basically tells you which GL this invoice should be categorized in, and this can be configured to offer multiple suggestions that can be appropriate. This makes the user’s task somewhat easier.

Learn and memorize repetitive tasks - once a user actually selects the GL code, the system can remember the selection and automate it the next time for the same vendor.

2. Fraud detection and error handling

Another crucial task that an accounting team has is catching errors before they happen. It might be as serious as wrong payment details and invoice fraud, or it might be as simple as a duplicate invoice.

Without a doubt, these problems are best prevented before they happen. Most organizations insist on making this process manual. However, having a human check each invoice makes things difficult because:

  1. It gives a single point of failure (and bottleneck) for the process - while it is good to have an employee check every expense for errors, sometimes things can slip through the cracks.
  2. It ensures that only the person with the most context on accounting entries (CFO/accounting head) can make corrections, and no one else. All the knowledge and financial data is only with a few people, and not spread across the organization.

How can AI help here?

Catching duplicates and wrong data is a piece of cake

Smarter duplicate detection/wrong information - Basic file duplicate checks verify only if the two files are the same.

With advanced AI technology doing the duplicate checks, you can go one step further - checking if the contents of two different files are suspiciously similar.

Multiple data validations on invoice data - Just auto-reading financial data from documents is of no use if someone has to login and verify it anyway. Advanced AI tools can now carry out data validation to ensure hygiene checks (for example, if a new bank account number on an invoice doesn’t match the usual one for a vendor, you’ll get notified!)

3. Learning simple actions that are repeatable

Ask any accountant on what they REALLY want AI to do, and this is the answer that comes out on top - many people feel that the real value of AI is when it can learn their patterns and save time for them.

For example, there are many small tasks that are done exactly the same way, for multiple types of invoices/receipts. Some examples:

  • Assigning an invoice to the right category/class/project in your ERP
  • Changing the GL mapping for one specific line-item of an invoice
  • Sending a particular vendor's invoice for approval to the same person, every time

How can AI accounting software help here?

The first stage is identifying the steps in the accounting process that are ideally suited to iterated re-learning (i.e., activities which you keep doing daily, that can eventually be memorized by the accounting software and automated 90% of the time).

Good examples of this are:

GL code assignment - The logic here is simple: if the application assigns the right GL code to an invoice, great! If not, you change it yourself, and the accounting AI remembers this change for next time. As a result, the automatic GL code assignment keeps getting better with every click you make.

Continuous learning will ensure your line items are automatically sent to the right GL

Category/Class/Project classification - If a particular vendor invoice can’t be auto-classified into the right category, AI can learn patterns in your selection (for instance, are you always classifying Uber receipts as "Project Costs" instead of "Travel"?). Over time, this becomes a rule-set within your platform, and is automatically applied.

How Nanonets can help you implement AI in your Accounting Process

The examples above are probably just the tip of the iceberg - there is a lot more that can be done for your AI accounting process that is only limited by how deep you are able to go into the process of automation and machine learning.

Fortunately, today accounting professionals do not have to be technically savvy in order to begin implementing AI capabilities into your accounting software - there are tools that allow you to get started almost immediately.

7 Ways You Can Use ChatGPT in Accounting | Nanonets
See how to use use ChatGPT in accounting to help simplify tedious tasks, generate financial projections quickly, create accurate audit reports, assist with invoice processing and expenses, and manage client communications effectively.

For instance, Nanonets is an AI platform that can transform your current accounting process, giving you valuable insights into your financial operations and those of your clients. It can do all that has been demonstrated above - and much, much more.

Simple to implement for small businesses and firms, yet complex in its capabilities, this is the ideal starting point for those looking to really step up their accounting process and scale their workload more efficiently.


Looking to add AI to your accounting and financial reports? Book a 30-min live demo to see how Nanonets can help your team implement end-to-end accounting automation.