Finance AI: How is AI Used in Finance and How Can It Improve Organizational Performance?

The office of the CFO is going through a new phase of transformation driven by artificial intelligence (AI). AI impacts how finance teams complete daily tasks and operations, and it has the potential to do much more. From automating repetitive tasks to uncovering hidden patterns in data and surfacing insights, AI has the potential to streamline operations, optimize workflows, and improve decision-making.

This article dives into the current applications of AI in finance operations and explores how finance leaders plan to use AI and generative AI (GenAI) in the future.

What is AI in finance and how does it work?

AI uses a combination of techniques, such as machine learning algorithms, natural language processing (NLP), and deep learning, to automate time-consuming tasks, improve accuracy of financial data, and provide valuable insights to make smarter financial decisions. The following are just a few examples of how these technologies work in finance departments:

• Machine learning algorithms learn from data to identify patterns, predict outcomes, and make recommendations. These algorithms are trained on robust datasets and can be used in critical areas such as  fraud detection and risk management.
• Natural language processing (NLP) can understand and extract meaning from financial data. NLP can be used for tasks like analyzing financial documents and automating report generation. For example, NLP can automatically read purchase orders and invoices, identify important information like supplier names, dates, and amounts, and then extract that into a structured format. It can also analyze text within invoices to identify suspicious transactions.
• Deep learning can be trained on historical data or image recognition, which can be applied to fraud detection by analyzing financial documents. For example, deep learning algorithms can analyze historical data on supplier performance, past disruptions, and industry trends to predict the likelihood of future problems. This allows you to prioritize risk assessments and focus resources on the best performing suppliers.
• Generative AI (GenAI) refers to a class of artificial intelligence algorithms that produce new and original data and content, such as text and images, by learning patterns from existing data. As it relates to finance, it’s like having a highly sophisticated virtual assistant that can analyze vast amounts of financial data, identify patterns, and generate valuable reports, contracts, or documentation autonomously.

Given these applications, many finance teams may already be using AI within their systems and processes without even realizing it.

How is AI used in finance?

With the understanding of how different AI technologies work in finance, there are probably a few use cases that now come to mind. AI is commonly used to automate tasks like data entry, expense report processing, and invoice processing with the goal of freeing up resources. Additionally, finance leaders use AI for data analytics, revenue and predictive forecasting, and decision-making because AI can identify missed trends and patterns. AI can also be trained to detect fraudulent activities and simplify regulatory compliance.

In our second annual Strategic CFO Survey, Coupa interviewed 500 CFOs across North America and Europe to understand how finance leaders are currently using AI and how they’ll invest in AI in the future. 100% of finance leaders reported that they’re already using AI to cut costs and increase productivity across different business areas, with nearly half (45%) planning to invest in AI to drive growth. The most finance leaders (31%) reported that they’re currently using AI in AP automation, followed by 29% using AI in procurement, and 28% of CFOs leveraging AI in cash and liquidity management. In the next 6–12 months, finance leaders plan to make the most AI investments in the AP automation (34%), followed by procurement (31%) and third-party risk management (29%).

Source: Coupa Clarity Report: The Strategic CFO

“The time to embrace AI is now. In today’s dynamic market, those who leverage technology to gain foresight and optimize operations will stay ahead. It’s not about replacing people with AI, but augmenting their capabilities and evolving alongside the technology.”

— Josh Waldron, VP Finance & Accounting, Scale AI

Examples of AI in finance

Many finance leaders have already integrated AI tools into their processes to increase efficiency by reducing manual tasks, enhance accuracy, reduce risks, and improve financial performance. Common examples of how AI is used in finance include:

• Detecting fraud and non-compliant behavior: Finance leaders use AI to reduce financial risks by automatically identifying and detecting suspicious activities and errors before they impact the bottom line and the company’s reputation. For instance, payment transactions that deviate from a user’s normal spending pattern or transactions originating from unusual locations can be flagged for further investigation. Additionally, AI-driven algorithms scan invoices and cross-reference them with purchase orders and payment histories. AI can detect duplicate invoices by recognizing patterns and similarities in invoice data that employees might miss or overlook. AI is also used to continuously monitor transactions and communications for signs of non-compliance with financial regulations, enhancing regulatory compliance.
• Automating contract analysis and data extraction: Traditional methods often involve manual review and data entry, which can be time-consuming and prone to human error. AI-driven solutions that use natural NLP and machine learning algorithms automatically extract critical financial data from contracts, including payment terms, obligations, and compliance requirements. This automation enables finance teams to quickly assess large volumes of contracts with higher accuracy. By reducing manual intervention, AI saves time for more strategic work, minimizes errors, and improves the consistency and accuracy of financial reporting. Additionally, this contributes to more effective risk management.
• Benchmarking against peers: Finance leaders use AI to benchmark their organization’s performance against peers and industry standards by analyzing vast datasets and key performance indicators (KPIs). This real-time comparison highlights areas of underperformance or success and provides prescriptions to guide improvements in cost reduction, resource allocation, and process efficiency. AI also uncovers industry trends and best practices, helping finance teams proactively adapt strategies. In today’s competitive environment, these insights are crucial for optimizing margins, driving efficiency, and identifying cost-saving opportunities.

Benefits of AI in finance

AI offers several advantages for finance teams to do more with less and complete tasks with more precision. Some of the key benefits include:

• Increased efficiency and reduced costs: AI automates repetitive and manual tasks like data entry, invoice processing, and generating financial reports. This frees up resources and staff for more strategic activities and analysis.
• Enhanced fraud detection and risk management: By quickly analyzing transaction data in real time to identify suspicious transactions or patterns that might indicate fraudulent activity, AI helps finance teams prevent financial losses and protect customer information. By analyzing historical data, market trends, patterns and shifts, AI enables smarter decisions through predictive analytics to minimize risks.
• Faster and more accurate decision-making: With the ability to analyze vast amounts of data, AI is able to identify patterns that finance teams might miss and make informed recommendations. AI-powered analytics also provide real-time prescriptive insights, enabling better forecasting.
• Streamlined regulatory compliance: By automating tasks such as data collection and reporting, AI can simplify regulatory compliance. This saves time and valuable resources while ensuring companies remain compliant ahead of any regulatory changes.
• Better industry benchmarks: AI-powered insights enable businesses to benchmark their performance against a global community to reduce risk, increase efficiencies, and improve margins and profitability.
• Navigate macroeconomic uncertainty with confidence: With automation, analysis of market shifts and patterns, and visibility of data, AI can provide enhanced recommendations to confidently navigate complex business issues and disruptions.

In addition to these benefits, CFOs and finance leaders hope to reap more benefits with GenAI. According to Coupa’s 2024 Strategic CFO Survey, 37% of finance leaders believe GenAI will provide strategic advantages to fraud detection, while 36% believe GenAI will enhance workflow process optimization. Thirty-five percent of CFOs say GenAI will provide strategic advantages to both data analysis and insights and supplier evaluation.

AI is only as good as the data it’s trained on

While AI provides a variety of benefits and is a central component of financial maturity, not all AI should be treated the same. The right data are crucial because AI solutions designed to support smarter decisions are only as good as the data they’re built on. Data scraped off the internet or based on surveys, collected for a few months or from a limited number of customers, and run through public large language models (LLMs) will train AI-driven solutions that reflect these limitations. Companies must understand the data organizations use and look for providers that use propriety, secure, and confidential data that have been safely and ethically sourced for years, from thousands of customers and millions of suppliers.

AI-powered finance: A Total Spend Management platform for all finance needs

In addition to providing the benefits above, Coupa’s AI Total Spend Management platform equips finance leaders with comprehensive data visibility and control by unifying the organization’s supply chain, inventory management, contracts, procurement, invoicing, and automated payments in one place. The easy-to-use platform boosts user adoption, enabling more collaboration for smarter decisions across the entire team and organization.

Through automation and AI, our platform guides finance leaders through uncertainties by leveraging real-time spend data and comparing an organization’s metrics against others to prescribe ways for the company to be more efficient, profitable, and sustainable. By leveraging AI trained on data from more than $6 trillion of real-time global transactional spend across a network of roughly 10 million buyers and suppliers for more than 15 years, Coupa’s platform provides intelligent insights and recommendations to take the guesswork out of decisions for smarter operations. By training our AI solutions on this proprietary “community intelligence,” Coupa ensures they provide precise, tailored KPIs and recommendations that account for each organization’s unique business rules, supplier dynamics, market factors, and operational constraints. This contextual awareness is pivotal in driving tangible, transformative value across the procure-to-pay cycle, enhancing margins, and improving profitability.