How AI Is Transforming Finance (2024)

1. Financial forecasting and planning

AI is transforming the financial forecasting and planning process through predictive analytics. Predictive analytics is a type of data analytics used in businesses to identify trends, correlations, and causation. It uses data, statistical algorithms, and machine learning to forecast future outcomes based on the analysis of historical data and existing trends.

Using predictive analytics, finance teams can forecast future cash flows using historical company data, as well as data from the broader industry. While traditional financial forecasts must be manually adjusted when circ*mstances change, AI-driven forecasts can recalibrate based on new data, helping keep forecasts and plans relevant and accurate. GenAI can even automatically create contextual commentary to explain forecasts produced by predictive models and highlight key factors driving the prediction.

2. Regulatory compliance

With the increasing complexity of regulatory compliance around the globe, the cost and resource burden of regulatory reporting has soared in recent years. Organizations devote significant time and resources to meeting those requirements. AI can take on a portion of the workload by automating compliance monitoring, audit trail management, and regulatory report creation.

A particularly valuable technology in regulatory compliance is natural language processing (NLP). NLP is a branch of AI that lets computers comprehend and generate human language. NLP is capable of quickly parsing through large amounts of textual data, transforming raw text or speech into meaningful insights. It can analyze lengthy documents, contracts, policies, and other text sources to extract critical information, pertinent changes, and potential compliance risks. NLP can even facilitate document management, automatically classifying documents based on predetermined criteria.

3. Cash flow optimization

Effective cash flow management always ranks high on the priority list of CFOs and their teams, and AI is proving to be a valuable tool in cash flow optimization. Due to the large amounts of data required, most finance professionals need more than a day to build a consolidated view of their cash and liquidity. And even then, forecasts can include errors and be quickly rendered obsolete.

Using predictive analytics and machine learning, companies can automatically compile data from all relevant sources—historical and current—to continuously predict future cash flows. With faster, more accurate cash flow forecasting, companies can make proactive moves to maintain healthy liquidity levels. For instance, if there is excess cash, they can take advantage of early payment discounts with suppliers or identify areas to reinvest in the business. When cash is tight, they can reassess loan positions or trigger foreign exchange transfers between subsidiaries. Finance teams also might use AI to optimize working capital by applying the right early payment incentives to select suppliers based on market conditions, payment history, and other factors.

4. Expense management

Expense management can quickly turn into a source of frustration. For employees, meeting expense policy rules by manually collecting receipts, filling out forms, and submitting expense reports is arduous and error prone. And finance teams can’t manually review every expense to ensure that all spend is compliant. AI is a powerful way to accelerate expense management and remove some of its complexity. For instance, optical character recognition (OCR)—a form of AI that can scan handwritten, printed, or images of text, extract the relevant information, and digitize it—can help with receipt processing and expense entry. OCR will scan uploaded receipts and invoices to automatically populate expense report fields, such as merchant name, date, and total amount.

The role of AI in expense management doesn’t end there. Companies can also use AI to automate approval workflows, flagging only the expenses that need the finance team’s review based on predetermined rules, promoting a “manage-by-exception” culture. AI-enabled expense assistants are also becoming more common, helping employees by automatically categorizing expenses, populating and filing the required documentation for each, and providing guidance around a company’s compliance policy.

5. Task automation

Perhaps one of the most common, and arguably one of the most impactful, capabilities of AI is task automation. AI can help automate numerous manual, time-consuming finance processes that used to inundate the finance team, including the following:

• Data entry: Extracts and inputs relevant information from statements, invoices, receipts, and other financial documents into a company’s system.
• Invoice capture and processing: Scans, reads, and digitizes paper invoices.
• Matching and verification: Automatically compares and matches invoices against corresponding purchase orders and delivery receipts.
• Payment execution: Facilitates the payment process, including scheduling payments to optimize cash flow, selecting the preferred payment method, and executing the payment automatically or with minimal human intervention.
• Reconciliation: Carries out an automated reconciliation process which matches payment transactions with bank statements to verify that amounts and beneficiaries align.
• Account code combination defaulting: Predicts and automatically defaults segment values of the account code combination on no-PO accounts payable invoice lines.
• Financial close processes: Intelligent process automation (IPA) automates the complex orchestration of the financial close and monitors task status across multiple systems, recommending new rules that guide ongoing automation, automatically kicking off close processes as soon as dependent tasks are completed and updating the close calendar.

Advanced automation of high volume, repetitive, and mundane manual tasks presents numerous benefits, including time and cost savings, decreased errors, and higher employee satisfaction as finance staff get to focus on more strategic, value-added tasks.

6. Financial reporting and analysis

AI can help automate and enhance multiple aspects of the financial reporting and analysis process. In the initial stages, it can extract relevant financial information from various data sources. It can then clean and process financial data by identifying errors, inconsistencies, or missing values and notifying finance staff of the areas needing attention.

AI can then use the data to help generate financial statements, such as income statements, balance sheets, and cash flow statements, transforming the data into reports that highlight key performance indicators (KPIs), trends, and observations. It can also help with regulatory reporting. GenAI can fill out the needed forms with data provided by the finance team for the staff to review and confirm.

GenAI can be used to produce narrative reports, providing context into the numbers by combining financial statements and data with an explanation of each. GenAI can even help prepare first drafts of 10-Qs and 10-Ks, including footnotes and management discussion and analysis (MD&A).

1. Increase efficiency and productivity

The list of ways AI can help increase efficiency and productivity in the finance department is already lengthy—and it’s just the beginning. The automation of numerous financial processes—such as data collection, consolidation, and entry—is already a notable add. It helps shift the role of finance from reporting on the past to focusing on the future, through analysis and forecasts that serve the company.

However, that’s merely the start of where finance could implement AI to drive efficiency and productivity. For instance, finance teams are also deploying GenAI to make it easier to find information, fill knowledge gaps, and get work done. Use cases include writing assistance, summarization, analysis, and chat. According to one 2023 study from Boston Consulting Group and MIT Sloan, GenAI improved a highly skilled worker’s performance by as much as 40% compared with workers who didn’t use it. A 2024 PwC report found that 60% of CEOs expect GenAI to create efficiency benefits. And a 2024 NVIDIA survey of 400 global financial services professionals found that “created operational efficiencies” was the AI benefit cited most often by those surveyed at 43%.

2. Improve customer experience and retention

AI helps enhance customer experience and retention by letting businesses deliver personalized, proactive, and integrated interactions across various touchpoints. Personalization is a good example. In a 2024 report by Forrester, 42% of executives surveyed identified the hyperpersonalization of customer experience as a top use case for AI.

AI can help deliver personalization by analyzing customer data, preferences, and behavior to provide the right product recommendations, content suggestions, and offers. Companies can also take it a step further with AI-driven customer segmentation for more-targeted marketing campaigns and promotions. AI can even help make pricing personalized, using real-time insights about individual customer preferences, market changes, and competitor activity to optimize price and discounts.

AI is becoming integral to customer retention with predictive analytics forecasting future customer behavior, lifetime value, and even churn likelihood, letting businesses focus their efforts on proactively addressing issues as they arise.

Lastly, AI-powered chatbots and digital assistants strengthen relationships with customers by answering questions on demand and providing fast, around-the-clock service.

3. Decrease errors

AI in finance can help reduce errors, particularly in areas where humans are prone to mistakes. High volume repetitive tasks can often lead to human error—but computers don’t have the same issue. Leveraging the advanced algorithms, data analytics, and automation capabilities provided by AI can help identify and correct errors common in areas such as data entry, financial reporting, bookkeeping, and invoice processing.

4. Reduce costs

AI is already showing the ability to help reduce costs. In the NVIDIA survey, more than 80% of respondents reported increased revenue and decreased annual costs from using AI-enabled applications. Further, AI implementation could cut S&P 500 companies’ costs by about $65 billion over the next five years, according to an October 2023 report by Bank of America.

AI can help reduce costs in many ways. Task automation is an obvious cost reduction tactic, letting companies decrease their labor costs, fill workforce gaps, improve productivity and efficiency, and have employees focus on strategic, value-adding activities. Companies also say that better insights and decision-making facilitated by AI is key to decreasing costs. Organizations using AI may be better able to optimize inventory levels and supply chains, detect fraud, identify cost-saving opportunities, and allocate resources more effectively.

5. Improve decision-making

A 2023 study by Oracle and New York Times bestselling author Seth Stephens-Davidowitz shed light on the dilemma faced by business leaders around decision-making—and the results were sobering.

Of the surveyed business leaders...

• 74% believed the number of decisions they make every day has increased 10X over the last three years.
• 97% wanted help from data in making decisions.
• 93% felt that the right decision intelligence could make or break an organization.
• 72% admitted the sheer volume of data has stopped them from making any decision at all.
• 89% believed the growing number of data sources has limited the success of their organizations.
• 94% felt the right data and insights could help the finance department make better decisions.

AI’s abilities around data management collection, analysis, and contextualization—just to name a few—help eliminate many of the decision-making roadblocks cited by business leaders.

6. Enhance risk management

AI is particularly instrumental in fraud detection. Trained machine learning models process both current and historical transactional data to detect money laundering or other bad acts by matching patterns of transactions and behaviors.

AI-based anomaly detection models can also be trained to identify transactions that could indicate fraud. AI systems in this case are continuously learning, and over time can reduce the instances of false positives as the algorithm is refined by learning which anomalies were fraudulent transactions and which weren’t.

AI’s capacity to analyze large amounts of data in a very short amount of time is an asset to the finance team. Whether it be analysis of supply chains, operations, or financial markets, AI can help quickly identify potential risks and use predictive modeling techniques to assess the likelihood and impact of possible outcomes.

7. Scalability

A major reason that AI is taking off now, and is accessible to such a broad base of companies, is because of today’s cloud-based AI platforms. AI systems, particularly generative AI, require a lot of computing power. The models are also frequently updated. Those two factors make it very hard to “buy AI” and run it in an organization’s own data center. Cloud computing platforms provide scalable infrastructure and resources for deploying and running AI applications, so companies pay for capabilities they need and enjoy updates without the need for patching and software updates. For companies that use cloud-based ERP systems, the incentive to use AI technology from the same cloud is substantial. There will be much less concern for moving and preparing data for AI if originating systems reside in the same cloud infrastructure.