Artificial intelligence (AI) is revolutionizing the finance industry, especially when it comes to accounting master data and its ability to understand and process this crucial component of any business’s financial management system.
Accounting AI is revolutionizing the finance function, when it comes to accounting master data. Its ability to understand and process a company’s historical data allows accounting team to reach new levels of efficiency and cost savings. Accounting AI leverages a company’s accounts, vendors, and transactional data to take AP Automation to the next level by powering Autonomous Invoice Processing. The first step to true and accurate AP autonomy is historical master data training.
What is AI Accounting?
To understand Historical Data Training, we must first understand what Artificial Intelligence (AI) is and how accounting AI works. In the most basic terms, Artificial Intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing objects in images, or coding invoices. Today, AI has become intertwined throughout our everyday lives. In fact, I bet that you use AI products every day. In the morning when you wake up, pick up your phone and it automatically unlocks by recognizing your face, that is AI at work. When you watch Netflix or Hulu and it recommends the next TV series you should binge, that is AI. And the latest AI craze, ChatGPT, has brought AI conversations to the dinner table.
AI Accounting, also known as "robotic accounting," refers to the application of artificial intelligence and machine learning techniques to automate and improve accounting processes. It involves using AI tools and technologies to analyze financial data, streamline accounting workflows, and enhance financial decision-making.
Here are some ways in which AI Accounting is being used:
- Data entry and categorization: AI algorithms can read and interpret invoices, receipts, and other financial documents, automating the data entry and categorization process.
- Expense management: AI can analyze expense data to identify cost-saving opportunities, monitor compliance, and streamline expense approval processes.
- Financial analysis: AI can analyze financial data to provide insights into financial performance, forecasting, and trend analysis.
- Fraud detection: AI algorithms can identify unusual patterns and anomalies in financial transactions, helping to detect potential fraud or errors.
- Duplicate invoice detection: AI can flag duplicate invoices to insure double payments do not occur
- Tax compliance: AI can automate tax compliance tasks, such as calculating tax liabilities, preparing tax returns, and monitoring tax filing deadlines.
Now that we’ve defined AI Accounting, how does this tool work? How is it different from AP Automation? Where does Historical Master Data Training fit into all of this?
To begin, let’s define what master data, as it relates to accounting, is and how data entry and categorization, expense management, financial analysis, fraud detection, duplicated detection, and tax compliance are all components of Vic.ai’s AI Accounting platform that provides AP autonomy to accounting teams.
What is Master Data in Accounting?
In accounting, master data refers to the essential information about the company's financial transactions, such as customers, vendors, materials, and accounts. Accounting master data is essential for accurate financial reporting and analysis, as it provides the necessary context and structure for understanding a company's financial performance. This information is typically stored in a database, like an Enterprise Resource Planning (ERP) system and serves as the foundation for all accounting activities.
When setting up AI Accounting to allow the platform to learn from your company’s past accounting behavior through the accounting master data. Below is detailed guide for what Vic.ai requires to effectively perform AI historical data training so the AI and machine learning algorithms can produce the most optimal output.
Chart of accounts
The chart of accounts is a structured list of all the accounts that a company uses to record its financial transactions. It provides a systematic way of organizing and tracking financial transactions, allowing businesses to generate financial statements, such as the balance sheet, income statement, and cash flow statement.
The chart of accounts typically includes a combination of balance sheet accounts (assets, liabilities, and equity) and income statement accounts (revenues, expenses, and gains/losses). Each account is assigned a unique account code, usually consisting of a numerical code or a combination of letters and numbers, to help identify and classify transactions.
Accounting Dimensions (location, project, class, etc)
In accounting, dimensions refer to additional categorizations that can be applied to financial transactions to provide more detailed reporting and analysis. These dimensions can include location, project, class, department, fund, or any other attribute that is relevant to the business. Dimensions are unique to each company, which allows for customization within a specific industry or function. For example, here are three different types of dimensions:
Location dimension: A company may have multiple locations, and the location dimension can help track financial transactions by the specific location in which they occurred. This can help identify trends or discrepancies between locations and inform business decisions about resource allocation, staffing, or expansion plans.
Project dimension: A project dimension can be used to track expenses, revenues, and resource usage for a specific project or initiative. This can help identify areas where resources are being overused or underused, as well as provide insights into the profitability of different projects.
Class dimension: A class dimension can be used to categorize financial transactions by different types of activities or products. This can help identify which products or services are most profitable, which areas of the business are performing well, and which areas need improvement.
Vendor master file
A vendor master file is vital part of accounting master data, as a database that contains essential information about a company's suppliers or vendors. It includes details such as the vendor's name, address, contact information, payment terms, and other relevant information required for the purchasing process.
Vendor master records are used to manage the purchasing process, track expenses, and maintain accurate financial records. They are also important for compliance purposes, as they help ensure that vendors are properly vetted and that payments are made in accordance with legal and regulatory requirements. Keeping the vendor master file up-to-date and accurate is essential for effective financial management and risk mitigation.
The vendor master file serves as a central repository of information that is used to manage vendor relationships and ensure that all necessary information is available to the purchasing team. It is typically managed by the accounting department and updated as new vendors are added or existing vendors are updated. In accounts payable, the vendor master file is used to create and manage vendor accounts, process invoices, and track payments.
Invoice attachments can be submitted to your accounting AI onboarding team as PDFs or other image formats. It’s best to collect several month's worth of historical invoices that focus on key vendors and provided as many invoices as possible that are tied to a single vendor. One hundred invoices per vendor is a good baseline.
General ledger (GL) accounts
General ledger accounts are a key component of a company's accounting system. They represent specific categories or types of transactions that are recorded in the company's general ledger, which is a central repository of all financial transactions.
Each general ledger account is assigned a unique account code or number, which helps to identify and classify transactions in the accounting system. Examples of general ledger accounts include assets, liabilities, equity, revenues, expenses, gains, and losses.
Financial transaction report
An accounting transaction report is an export from your accounting system to correlate line items to the invoice images and other data that the AI will ingest (invoice numbers, due dates, issue dates, GL codes, vendors, and more).
Vic.ai’s Accounting AI training model requires copies of the actual invoices and the information about the expense and General ledger codings in order to determine which expense line item in the transaction report matches which invoice image file.
Why accounting AI master data training is important
Think about a time when you told someone you were 90% sure about something. What made you so confident? Most likely, you looked back at all the previous similar decisions you made, and using that context, you were able to arrive at the 90% confidence mark. That same concept applies to AI. An ‘algorithm’ looks back at similar situations and how the problem was answered in the past to make a prediction about the future. We leverage Historical Data Training so the AI has a springboard to jump off from. The success of an AI system depends on how accurately it can make predictions, and that accuracy is directly proportional to the quality and quantity of data the system is trained on. Ultimately, AI is an algorithm that uses data that it has seen in the past to make predictions about the future.
High-quality data is essential when building successful AI models. The consequences of poor data can be severe, leading to inaccurate predictions and unreliable decision-making. Consider Ray Bradbury’s seminal dystopian novel “Fahrenheit 451.” In the story, the government uses a false narrative about Ben Franklin to rationalize the burning of books. Ben Franklin’s image in history was distorted to reimagine him as someone who started fires instead of putting them out. This distortion allowed the government to point to a historical moment in order to justify book burnings controlling the population’s thoughts and actions and consequently changing the future.
Just like in the story, poor data or false histories can lead to misleading outcomes. If we feed an AI model inaccurate or incomplete data, the outcomes it produces will be flawed. Inaccurate data is like starting a fire instead of putting one out, as it can lead to the destruction of the AI’s effectiveness and reliability. Historical Master Data Training, along with trustable data, is essential to ensure the AI is making informed decisions.
How accounting AI master data training works
At the end of the historical data training, the goal of the system is to reduce invoice processing times and learn from past mistakes — just like a human would! This way, your AP automation software is caught up to speed with high accuracy rates from the start.
The importance of Historical Accounting Master Data Training can be seen in practical applications, like coding invoices or fraud detection. With coding invoices, historical data allows the AI to understand the patterns of previously processed invoices. This will help the AI make more accurate predictions in the future, shortening processing times and eventually eliminating any need for humans to manually code invoices.
For example, the AI already knows that the Salesforce.com expense will likely be attributed to the sales team. The AI is intelligent enough to predict all the values necessary to properly code an invoice from the header level fields, such as invoice number or invoice date, to the line item fields that are not explicitly on the invoice. Because of historical data training, the AI has seen previous examples of a Salesforce.com invoice and knows the proper vendor (Salesforce.com Inc), GL account (1541-Prepaid Software), and dimensions to code to (Location = San Francisco). When you become a Vic.ai customer, your new accounting AI system will go through extensive historical data training. This is the time when your system will “learn” from your team’s past invoice processing actions to get steps closer to AP Autonomy. (On average, this training period will last about two weeks.)
How accounting AI uses your historical master data to reach high accuracy rates and invoice processing speeds
The benefits of Historical Data Training extend far beyond accuracy and efficiency because the AI is constantly learning, allowing models to adapt and change over time. As new data becomes available, the AI models can refine their predictions and become even more accurate. This is especially important in industries such as finance or healthcare, where new data and research is continuously emerging.
Without quality data, AI models can produce misleading outcomes and false narratives, leading to unreliable decision-making. The success of AI systems is heavily reliant on the quality and quantity of data they are trained on, and successful AI models will need to protect the sanctity of their data sources. Leveraging Historical Master Data Training as the starting point, AI models will adapt to changes over time, making more accurate predictions and improving their efficiency. As AI continues to revolutionize the way we interact with the world, it is essential to understand the importance of Historical Data Training that makes it all possible.
Eliminate AP bottlenecks - make invoice approval effortless
Payments move at the speed of invoice processing, which can be delayed by manual coding and approval routing. The average invoice requires several steps and people to approve before its fully processed. Watch the AP Intelligence Discussion recording below to learn how to eliminate AP bottlenecks and see the end result of historical master data training.
The evolution of PO Matching: past, present, and future solutions
The evolution of PO Matching: past, present, and future solutions
The must-listen-to AI and Generative AI podcasts 2023
The must-listen-to AI and Generative AI podcasts 2023