Businesses today have access to incredibly powerful AI capabilities that can transform financial operations. Dubbed “generative AI,” these models can simulate human judgment and reasoning to accomplish tasks like interpreting regulations, processing invoices, evaluating market conditions and generating presentations. But generative AI in finance also poses unique challenges around data privacy, bias and more. Constructing an ethical framework to govern generative AI is key to realizing the technology’s full potential while safeguarding stakeholders.
Traditional AI vs. Generative AI: Why Generative AI Is So Dynamic
Traditional AI models in finance have focused largely on data analytics, offering predictive insights based on existing data. Generative AI, however, introduces the ability to create and simulate, thereby expanding the scope of AI’s application in finance. While traditional models excel in analyzing data trends and relationships—typically with numerical data, but not always—generative AI can create new financial scenarios and offer innovative solutions for complex financial challenges. This fundamental difference makes generative AI a dynamic tool to use in conjunction with traditional machine learning models.
The emergence of large language models (LLMs) like ChatGPT or Bard also allow businesses to complement traditional AI models by processing and analyzing text-based data. This synergy allows for a more holistic view of financial operations, combining quantitative analysis with qualitative insights.
High-Impact Use Cases for the Finance Function
Flexible reasoning allows generative AI to excel across a variety of financial contexts, from simulating scenarios to streamlining report and document creation for finance professionals.
Generative AI in accounting and finance can be used in a number of ways, such as:
- Automating repetitive, high-volume tasks like invoice processing and reconciliations to boost productivity or detect errors and fraud.
- Reviewing earnings calls, filings and news to derive actionable investment insights
- Simulating customer scenarios to create fine-tuned financial products and pricing models.
- Harness publicly available data via large language models (LLMs) to craft valuable market insights or persona-based insights.
- Forecasting financial performance under different assumptions and market conditions.
- Suggesting creative options for raising capital, cutting costs or improving cash flow.
- Analyzing decades of financial records and external data to reveal growth opportunities.
- Interpreting complex regulations and surfacing relevant compliance considerations.
- Drafting financial reports, presentations, memos and other documents by synthesizing information from various sources.
- Developing training materials or simulating financial scenarios to facilitate development.
With so many groundbreaking applications, businesses have a wealth of opportunities to derive value from generative AI in finance and accounting. However, this doesn’t mean that these tools are infallible or at a stage where human intervention is no longer needed. As with any transformational technology, users must take steps to prevent potential downsides.
Confront AI Risks From the Get-go
Imagine a media conglomerate uses generative AI to produce monthly financial reporting narratives and variance analysis for business unit leaders. Though efficient initially, a new revenue recognition standard gets implemented mid-year. As the untouched AI system continues generating inaccurate analyses, faulty insights threaten strategic decisions on production budgets and spend.
Now imagine that an FP&A team at an e-commerce retailer directs generative AI to construct demand forecasts by customer segment. The systems underestimate post-holiday returns and fail to incorporate shipping snafus, throwing off supply chain stocking budgets.
While promising, generative AI does come with certain risks. Businesses that adopt generative AI tools without the necessary controls in place may experience:
- Inadvertent exposure of private customer or financial data, leading to violations of regulations or data governance policies.
- AI presenting biased, exaggerated or otherwise misleading information, especially with regards to regulatory changes.
- Teams becoming over-reliant on AI.
Fortunately, following responsible AI best practices can help minimize these dangers.
How to Responsibly Deploy Generative AI in Finance & Accounting
While generative AI introduces tremendous promise for financial tasks, it’s imperative that businesses use the technology as an enhancement tool rather than outright replacement for finance professionals. Humans must remain firmly in charge of critical functions.
Instituting generative AI in finance without thoughtful governance is a recipe for problems like biased outputs, loss of institutional know-how or regulatory missteps. Instead, finance leaders should structure generative AI deployment to augment existing staff and workflows. The human review stage is vital—no materials should reach end users without savvy financial experts double checking AI outputs first.
Stringent controls must align authority with accountability to prevent over-reliance. Provide transparency into the AI’s limitations so professionals can contextualize outputs appropriately, and mandate AI competency training to aid human-machine collaboration.
The keys are using generative AI judiciously as a collaborator rather than a decision maker. This framework will allow finance leaders to harness incredible new capabilities responsibly.
Combine Generative AI With Human Expertise
Generative AI represents a watershed moment for elevating finance functions. But as with any powerful technology, users must manage risks thoughtfully. By creating ethical frameworks, providing transparency and keeping humans in charge, businesses can realize generative AI’s full potential safely, responsibly and sustainably.
Deploying powerful new innovations like generative AI requires finding the right balance between human talent and technology. But many finance teams struggle with talent gaps that could undermine AI governance. Paro’s on-demand finance and accounting talent platform lets businesses flexibly access financial experts to collaborate on emerging capabilities. Need a pricing expert to double check simulated models? A compliance professional to vet generated reports? Or, a presentation master to put the finishing touches on AI-drafted investor materials? Schedule a free consultation to find the right expertise for your needs.
