Four Ways GenAI is Transforming Business Financial Management

The arrival of generative AI will have significant ramifications for virtually every industry on Earth. One of its biggest impacts will be felt in financial management at an enterprise level, with the efficiency on show capable of cutting costs and optimizing existing processes.

The chances are that you already know plenty about generative AI. Unless you’ve been living under a rock, you’re likely to have either seen or used ChatGPT and had a taste of the limitless potential of the technology.

This is only the beginning. Forecasts suggest that generative AI will fundamentally change 52% of jobs as we know them today, and the technology can potentially enhance a number of existing roles through automation.

With the ability to actively interpret masses of data and generate documents, insights, and actionable advice from these unstructured datasets, GenAI will become an asset in finance and accounting across several sectors.

What could this mean for your financial management? Let’s explore four examples of how the generative AI boom is set to change your operations forever:

1. The age of predictive analytics

Generative AI can analyze large datasets, directly improving the ability of financial departments to uncover insights and create detailed financial reports that aren’t subject to human error.

Built on machine learning insights, Accenture data suggests that 42% of companies experienced profitability from their ML and AI initiatives that exceeded expectations, with only 1% expressing disappointment.

When using unstructured data, generative AI models can use predictive analytics to identify patterns in historical data within your business model, offer rich insights into financial trends, and offer actionable advice based on possible future outcomes.

New applications in accounting

These innovations can help transform accounting as we know it into a more proactive part of business operations. Rather than record-keeping, business leaders can access smarter decision-making that can help ensure financial health throughout different departments.

We’re already seeing globally focused firms like EY utilize generative AI systems to improve efficiency in their accounting. The consulting giant has also incorporated large-language models (LLMs) to solve international payroll queries from employees. These models draw on complex legal and regulatory datasets to offer accurate and efficient answers without relying on human response times.

These insights can prove invaluable for startups during funding rounds, allowing resources to be more appropriately allocated to ensure financial sustainability.

2. Next-generation documentation

Because generative AI can process, summarize, and extract relevant data from extensive financial documents like annual reports, quarterly earnings, and other financial statements, it is an excellent tool for focused analysis and driving decision-making processes.

This means that GenAI algorithms can generate abridged reports for ease of reference and provide the necessary headlines for relevant parties to use without having to spend time sifting through information when faced with a deadline.

KMPG data suggests that 65% of reporting leaders are already using artificial intelligence and generative AI solutions in their workflows. Additionally, 71% anticipate that AI solutions will become imperative in the future, while 48% have already adopted these solutions.

The adoption of generative AI workflows offers leaders several benefits, from staff efficiency and ease of workload to more accurate data and cost-saving insights.

Incisive reporting

Data extracted from documents can also be used to create new essential documentation autonomously and can be used alongside invoice generators to accurately deliver invoices to contract staff, freelancers, and small business owners on the fly without the risk of error in the data entry process.

Because of its unprecedented capabilities, Deloitte’s analysis of generative AI suggests that financial reporting could transcend text-based insights. Instead, speech and audio can be generated to supply high-quality narration and dubbing on videos and presentations, images and video may be used to deliver hyper-realistic insights and graphics based on text inputs, and 3D objects can be rendered from 2D inputs to create data-driven virtual environments.

3. Perpetual compliance

Businesses utilizing generative AI can also tap into the technology’s machine-learning capabilities to craft an intelligent regulatory and policy compliance monitoring tool to ensure constant operational efficiency.

The technology can monitor regulatory changes, procedural requirements, and policy shifts to alert relevant parties when a rule change puts the business at risk or whenever compliance lapses in certain areas of operation.

While remaining on the lookout for compliance misalignment, generative AI is capable of automating finance compliance on a perpetual basis for businesses, saving countless departmental hours in the process.

One example of perpetual compliance in action is LeewayHertz’s generative AI platform, ZBrain. This platform seeks to optimize compliance processes while offering regulatory adherence for businesses and optimizing governance practices.

Using client data to train advanced LLMs like GPT-4, Vicuna, Llama 2, or GPT-NeoX, ZBrain can integrate regulatory compliance into enterprise processes to boost efficiency without the risk of running into issues.

This tool and all appropriately trained generative solutions can be handy for internationally-focused businesses that may need to monitor compliance across borders in real time.

4. Real-time Analysis

While human financial analysis can be a lengthy process, generative AI offers real-time insights that can be actioned and delivered in seconds.

The implementation of AI solutions in financial reporting is nothing new. KPMG data suggests that almost three-quarters of businesses are already using artificial intelligence in financial reporting, and this figure is set to rise to 99% in three years.

With the rise of AI solutions in financial analytics, decision-makers need to explore how generative AI can drive efficiency today rather than tomorrow.

This ability to offer up-to-the-minute insights into a company’s financial health can help quickly alert decision-makers to anomalies within the data, emerging trends, and possible risks to the business’s bottom line as they occur.

Alongside predictive analytics, real-time monitoring for financial trends allows businesses to react faster to issues in their data to guard against issues and leverage more informed decision-making.

Applications in efficiency

This can be a particular asset when safeguarding against financial crime. For example, risk management firm NICE Actimize developed a GenAI tool to support human staff in investigating financial crimes.

The tool was capable of cutting investigation time by 50% and saving as much as 70% when filing suspicious activity reports (SARs).

Aside from financial crime, we’re also seeing rapidly emerging use cases surrounding financial analytics. Deutsche Bank, for instance, is testing Google Cloud’s generative AI and LLMs at scale to deliver fresh insights for financial analysts, helping to improve real-time execution velocity and operational efficiencies.

Implementing Generative AI Management

Generative AI will have a lasting impact on business financial management. Although use cases are emerging frequently, the path from ideation to implementation will drive innovation throughout the sector.

With the ability of GenAI to predict and monitor performance in real time, decision-makers will no longer have to fear unexpected, nasty surprises in expenditures or the damaging effects of losing compliance with regulators.

Most importantly, the technology’s generative capabilities will save countless hours for financial and accounting teams, helping to deliver greater accuracy and efficiency throughout one of the most important areas of operations for all businesses.