Top 7 Use Cases of Generative AI in Accounting

Autonomous accounting, powered by advanced technologies such as artificial intelligence (AI) and hyperautomation, is revolutionizing enterprise accounting by automating financial processes, decision-making, and services. Companies are increasingly adopting autonomous finance due to its potential to enhance efficiency, reduce operational costs, and improve customer experiences.

Generative artificial intelligence, or GenAI for short, is revolutionizing accounting by automating financial processes and facilitating decision-making without human intervention. While there are many potential functions that can be reimagined using GenAI, in this article we cover the top use cases for accounting where this innovative technology can make a big impact.

What is generative AI?

GenAI refers to a class of artificial intelligence (AI) algorithms that can generate or create new content, such as text, images, music, and even video, without any direct human intervention.

In accounting, GenAI can have a significant impact by automating tasks that were previously done manually, such as generating financial reports, performing data analysis, and creating forecasts. It can also be used to analyze large volumes of financial data, identifying trends and patterns that would be difficult for a human to detect.

According to KPMG, “Generative AI for finance helps organizations accelerate their path to greater efficiency, accuracy, and adoptability.”

Generative AI use cases

Reaching “AP autonomy” across the accounting function requires strategic planning, an implementation and maturity plan, and leveraging the right AI technology. It’s important to select a particular area to pilot GenAI solutions into your operational strategy successfully before embracing enterprise-wide. Here, we’ve outlined the top seven GenAI use cases for accounting and finance teams:

1) Document processing and analysis

Generative AI can process, summarize, and extract valuable information from large volumes of financial documents, such as annual reports, financial statements, and earnings calls, facilitating more efficient analysis and decision-making. GenAI could also reduce the need for custom software implementations related to traditional intelligent document processing (IDP) solutions, which often require custom setup and training through professional service providers.

2) Conversational finance

GenAI models can be used to create more natural and contextually relevant responses in AI-powered chatbots or virtual assistants for conversational finance. As a result, GenAI can significantly enhance the performance and user experience of financial conversational AI systems by providing more accurate, engaging, and nuanced interactions with users. It can be used externally for customer conversations, as well as internally within departments, such as moving an invoice through the approval cycle.

3) Financial question answering

GenAI can provide accurate and detailed answers to financial questions posed by users, thanks to its understanding of human language patterns and its ability to generate coherent, contextually relevant responses. These models can be trained on large datasets of financial knowledge to respond to a wide range of financial queries with appropriate information.

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4) Financial report generation

GenAI can automatically create well-structured, coherent, and informative financial reports based on available data, streamlining the reporting process and reducing manual effort. Moreover, GenAI models can generate customized financial reports or visualizations tailored to specific user needs.

5) Financial analysis and forecasting

By learning from historical financial data, GenAI models can capture complex patterns and relationships in the data, enabling them to make predictive analytics about future trends, asset prices, and economic indicators.

GenAI models, when fine-tuned properly, can generate various scenarios by simulating market conditions, macroeconomic factors, and other variables, providing valuable insights into potential risks and opportunities.

In a KPMG survey, 83 percent of respondents use AI for financial planning, including predictive models, scenario creation, and budget insights.

6) Fraud detection

Because GenAI can process massive amounts of data in real real-time, it can flag anomalies for immediate review and prevent fraudulent activities.

GenAI can also generate synthetic examples of fraudulent transactions or activities to help train and augment machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial data, leading to faster detection and prevention of fraud.

7) Sentiment analysis

GenAI can be used for sentiment analysis to measure brand reputation and customer satisfaction through social media posts, news articles, or other sources. By gaining insights into customers’ emotions and opinions, companies can devise strategies to enhance their services or products based on these findings.

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Autonomous invoice processing - a viable use case for GenAI

Beyond the 7 use cases listed above, one of the most manual and time-consuming areas of accounting that can benefit from GenAI is invoice processing. GenAI can be leveraged for complete end-to-end invoice processing, from ingesting invoice data, reading and categorizing invoices, routing documents through workflows, handling approvals based on patterns, and executing payments.

Vic.ai, an AI-first platform for accounts payable, is one example of a company leading the autonomous accounting movement. Vic.ai leverages AI technology for invoice processing and payments to improve speed, accuracy, and scalability, ultimately enabling their customers to shift focus to more strategic, meaningful work. By processing more than half a billion invoices with up to 99% accuracy, Vic.ai has helped 10,000+ customers achieve nearly $200 million in cost savings.

Overall, GenAI has the potential to transform accounting by automating routine tasks, freeing up time for financial professionals to focus on higher-value tasks, and improving the accuracy and reliability of financial reporting and analysis.

This article originally published May 2023, and was updated March 2024.