GenAI for finance and accounting: Do we have the right data and use cases?

Generative artificial intelligence (GenAI) holds significant potential for the finance and accounting (F&A) function, but as with any emerging technology, it’s hardly a straight shot to success. GenAI could boost the finance function’s ability to better understand company and market data toward minimizing business risks and unearthing impactful insights to make informed business decisions and build effective strategies.

It can use synthetic data in combination with traditional AI approaches to simulate real-life financial scenarios. GenAI’s implication for the finance function cannot be ignored. We already see potential applications in transactional and analytical finance activities, including forecasting, risk analysis, and fraud detection (see Exhibit 1).

Exhibit 1: Gen AI can bring efficiency across the F&A value chain

Source: HFS Research, 2023

GenAI can contribute to improved financial forecasting by analyzing large volumes of data to generate accurate predictions about financial performance, potential risks, and market trends, allowing organizations to make more informed decisions on their strategic moves. It can minimize fraudulent activities by identifying patterns and anomalies in the financial data. Integrating GenAI can help assess large financial documents and streamline and create financial reports. It can also help eliminate irregularities in billing and collections processes.

During our recent conversations with finance leaders, a CFO of a financial services company highlighted, “GenAI wasn’t mainstream until a couple of months ago; our technology team has started looking into it. There are going to be things that will be helpful, but we’re early in our journey on generative AI.”

Finance functions are embracing GenAI’s potential value, and business services providers are already on the path of new value creation—but it’s still the early days of piloting and testing use cases.

Business service providers are actively building and piloting Gen AI use cases in response to client demand

Business service providers across the F&A value chain are building GenAI solutions in line with existing clients’ common problem areas. They are experimenting with use cases that could lead to improved business performance and profitability across several transactional and strategic finance functions:

• Accenture has several GenAI pilots in motion with its F&A clients, including use cases to potentially review, understand, and automatically translate large volumes of data documents, automate reporting, create financial statements, manage contracts and negotiations, report liquidity, detect fraud, model scenarios, and reconcile invoice reports.
• Infosys has built multiple use cases on its Infosys Topaz platform, including a financial reporting use case to generate reports directly from financial BI systems using internal and external lenses. Other use cases include Answer Smart and Insight Smart.
• IBM employs artificial intelligence to enhance clients’ claims and deduction management processes, wielding the potential to substantially influence working capital and diminish claims processing duration. This initiative encompasses a meticulous mapping of every step and activity inherent in claims validation, from the generation of a claim or receipt of a ticket to its ultimate resolution. The overarching objective is to instill efficiency and advancements in the process, fostering cost reduction and reducing revenue leakage.
• Genpact is building use cases for the accounts payable help desk, collections, financial reports generation, conversational finance, and financial performance reporting to drive insights, enhance efficiency, and reduce manual intervention.
• EXL integrated a GenAI model into its digital finance suite (DFS) that leverages past email data for data-led payables management. It sends automated responses to supplier queries, extracting information from emails sent to process invoices. It uses GenAI to provide future trends on financial KPIs and predictive insights to highlight areas of improvement and opportunities to enhance business performance. Also, EXL has embedded conversational BI to respond to queries from the CFO organization.

GenAI has undoubtedly created a sense of urgency among leading service providers to find the best use cases to help their clients streamline day-to-day financial operations, significantly aiding better decision making, saving time, and minimizing process redundancy and errors. Success will depend on how well they can infuse GenAI outputs into core F&A operational workflows, along with the data maturity of their clients.

The right data, ethical practices, and mindset shifts are a must for successful GenAI integration

Given the finance industry’s risk-averse nature, we expect GenAI use cases in this area to prioritize data governance. Protecting sensitive financial information and ensuring data integrity are crucial in F&A; thus, data security and authenticity will need to be front and center of any GenAI F&A solutions. The CFO of a consumer goods company shared during our recent discussion, “At the moment, we have concerns around data privacy and security. We will be using it in different forms, and the usage of GenAI will come in faster in the form of writing emails for chasing payment from the customers to begin with.”

While GenAI’s benefits are immense, getting the data house in order is the most significant action for enterprises to ensure the accuracy and training of the GenAI model to build a strong foundation. At the same time, it is crucial to align with regulations and standards to ensure that the AI algorithms are free from bias and hallucinations, transparent, and compliant with data security protocols to prevent misuse of any financial data sensitive and confidential to the organizations.

The Bottom Line: GenAI infusion into F&A has already begun, but it brings us right back to the overarching need for better data quality and governance in finance.

Overall, F&A will undoubtedly experience a significant impact from GenAI in the next few years, and the finance function will gradually adapt to it. Most CFOs we talk to today express their desire for finance talent to become more analytical and strategic when interacting with the business. Arming talent with GenAI assistance can only help with that journey, whether by providing analytical insights and recommendations or automating aspects of their jobs to give them more time for value-added activities. Sorting through the data silos and safeguarding the processes from bias and data threats will be the key to success. Enterprises already on their journey of data-driven finance will be in the best position to start to make progress with GenAI.