What Are The Different Types of AI in Accounting?

Emily Perkins

Emily Perkins

Head of Content Strategy

In this article, we look closer at different types of artificial intelligence (AI), as well as traditional automation methods, and how they are being used in accounting.

March 22, 2024

7 min read

What Are The Different Types of AI in Accounting?

The business world is buzzing about artificial intelligence (AI) and how it can achieve new efficiencies, rebuild processes, and influence the way humans approach their day-to-day tasks. However, it is confusing and often overwhelming, especially when many traditional software companies are now adding AI features or functionality. Sometimes, these solutions haven’t changed much from a foundational perspective, yet are quickly trying to ride the AI wave with new product names, simple add on tools, or marketing tactics.

In this article, we break down the different types of AI and how they are used in accounting and finance to help you navigate through the fluff and find the right AI tools for your organization and team. We also look at more traditional automation methods, such as optical character recognition (OCR) and robotic process automation (RPA), and their role in accounting. And, we explore how AI-first technology solutions built leveraging AI from the ground up ensure “AI authenticity,” where proprietary AI algorithms and code have been engineered explicitly for the accounting function.

What is AI?

Artificial Intelligence (AI) replicates human intelligence actions and processes with machines, particularly computer systems. It encompasses various techniques like machine learning, natural language processing, and computer vision to mimic human cognitive functions. AI offers automation, data extraction and analysis, and decision-making capabilities in accounting.

How is AI used in accounting?

AI transforms accounting by automating repetitive tasks like data entry and invoice processing, enhancing accuracy and efficiency. It leverages algorithms to analyze financial data, detect anomalies, and generate insights for informed decision-making. From predictive analytics to fraud detection, AI optimizes processes, empowering accountants to focus on more strategic initiatives. Moreover, by offloading mundane tasks, AI can liberate human resources to focus on more strategic activities like analysis and decision-making, maximizing productivity and driving business growth.

What is an AI algorithm?

An AI algorithm is a complex set of computed instructions for performing specific tasks or solving problems by processing data. These algorithms learn from data patterns, make predictions, and adapt over time, powering AI applications across various industries.

How are AI algorithms used in accounting?

AI algorithms can analyze financial data to identify trends, predict future outcomes, and automate tasks like budgeting and forecasting. They can facilitate better decision-making by providing actionable insights and streamlining complex processes like risk management and financial reporting.

What does “proprietary AI” mean?

Proprietary AI refers to AI technologies developed and owned by specific organizations. These solutions are designed to address unique challenges or requirements, offering competitive advantages and customization options.

How is proprietary AI used in accounting?

In accounting, proprietary AI solutions automate tasks like expense categorization, invoice processing, data extraction, document matching, and financial analysis. They enhance efficiency and accuracy and are scalable, meeting the distinct needs of organizations while ensuring data security and confidentiality.

What is generative AI?

Generative AI (or GenAI for short) is an AI subset that can create new content based on learned patterns from historical data sets, such as images, text, or audio. GenAI can generate diverse outputs, fostering creativity and innovation across industries.

How is generative AI used in accounting?

GenAI assists in generating synthetic financial data for testing software, simulating scenarios for risk analysis. Producing realistic outputs enhances decision-making and can augment traditional accounting practices. In accounting specifically, the top use cases for GenAI are document processing and analysis, conversational finance, financial report generation, financial forecasting, fraud detection, and sentiment analysis.

What is Robotic Process Automation (RPA)?

Robotic Process Automation (RPA) tackles repetitive tasks in accounting by employing software robots or "bots" to execute rule-based processes traditionally handled by humans. These bots emulate human actions, following predefined rules and templates to interact with digital systems. RPA can act as a virtual workforce, executing tasks with speed while ensuring compliance with established protocols. 

How is RPA used in accounting?

In accounting, RPA streamlines data entry, reconciliation, and report generation processes. By adhering to predefined rules and templates, RPA bots consistently automate routine tasks, reducing the likelihood of errors. This automation not only enhances operational efficiency but also ensures compliance with regulatory requirements. 

However, RPA typically works best with structured data, and often requires augmenting with AI to handle unstructured data, new data patterns, or data anomalies that don’t fit into a typical template or rule. Financing and accounting teams often face variance within invoices, payments, and other day-to-day data, and can be limited when only automating with RPA.

What is OCR?

Optical Character Recognition (OCR) serves as a cornerstone technology in digitizing and processing textual information from various document formats. It enables the conversion of scanned images or PDFs into editable and searchable text, thereby facilitating efficient data extraction and analysis.

How is OCR used in accounting?

OCR automates data entry and extraction processes in accounting, particularly from financial documents like invoices, receipts, and statements. By recognizing and interpreting text within these documents based on predefined templates or rules, OCR can accelerate workflows and enhance accuracy in capturing critical financial information. This technology eliminates the need for some manual data entry but also improves data accessibility and analysis capabilities, empowering accountants to make more informed decisions.

While OCR can quickly convert structured text for data entry tasks, AI is better suited for more complex tasks, multi-step processes, variance within document structures and elements, and more. 

This short video explains more about the differences between OCR, RPA and AI.

The role of training data in AI

Training data plays a fundamental role in the development and performance of AI systems. It is the foundation upon which AI algorithms learn to recognize patterns, make predictions, and generate insights. Master data sets, which encompass comprehensive and well-curated data collections, are particularly crucial for effectively training AI. These master data sets provide diverse and representative examples that enable AI models to generalize and perform accurately across various scenarios. By exposing AI algorithms to a wide range of data points, including edge cases and outliers, training on master data sets helps mitigate biases and improve the robustness and reliability of AI systems. Ultimately, the quality and breadth of training data significantly influence the performance and efficacy of AI applications, underscoring the importance of leveraging master data sets in AI development.

What kind of AI does use?’s autonomous finance platform leverages proprietary AI to process invoices faster and more accurately than traditional template and rules-based automation solutions. Invoices are automatically ingested from various sources, including electronic file formats, EDIs, email, PDFs, direct connections, and more. Once extracted, the AI reviews and classifies the invoice data, then matches and processes all relevant invoice information, including vendor, dates, numbers, cost accounts, dimensions, assets, and purchase orders. 

Autopilot is a unique feature for invoice processing that indicates to the user that all the invoice details are predicted with 95 percent confidence and do not require human review. Autopilot sends invoices to approvers without requiring any data entry or classification review beforehand. can also detect and surface early payment discounts so customers can take advantage of every opportunity to save on their outstanding invoices. The AI technology can extract discount information from invoices, enabling customers to see where they can save money and then act on the opportunities, improving cash flow management.

The proprietary AI used for the autonomous finance platform has trained on a significant volume of accounts payable data to achieve the accuracy rates that customers experience. software has trained on over half a billion invoices, which means the AI has extensive historical knowledge of invoice structure, formats, anomalies, and much more. When this AI is leveraged on a new customer’s invoice data, the knowledge gained by the data training ensures a faster ROI.

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