The implications of generative AI in Finance

Generative AI-powered predictive models may review large data sets in fractions of the time, revealing immediate and ongoing trends, and enabling real-time monitoring and forecasting. With time, we are likely to see generative AI models capable of advanced scenarios and impact analyses—with the potential to run simulation after simulation, altering small variables along the way, to give leaders the insightful spark they need to move forward in new, productive, and profitable directions.

Activities like billing, payments, and collections may be largely automated through next-level portals, saving companies time and reducing costs. Transactional inquiries could be further transformed with generative AI-powered “agents” able to deliver tailored content based on personal needs and communication preferences. Generative AI could also further improve finance analyst efficiency by generating new standard operating procedures on the fly, capturing minor changes in process, and enabling conversational Q&A using models trained on enterprise data to support new employees through the procedures.

Highly reliable, generative AI-produced drafts for internal and external financial reporting may save considerable time during month and quarter end. Generative AI could also be used to automate activities like reconciliations, journal entries, and financial consolidation—tying out financials with mathematical accuracy and balancing. Generative AI could also assist in drafting periodic management reports in both numeric and narrative formats—allowing organizations to be more efficient and potentially catching changes in costs, buying patterns, or market changes—earlier and faster than ever before.

Generative AI may enhance risk management processes by enabling unlimited, simultaneous, continuous anomaly detection—analyzing transactions in real time and catching discrepancies immediately. Generative AI could monitor the connectivity between financial systems and data lakes to alert finance technology teams of issues. With time and development, generative AI-enabled systems not only could flag suspicious activity, but also might analyze it and implement response mechanisms. The systems could spot anomalies and take proactive security measures, such as creating action reports, providing recommendations, and notifying affected users.

Automation is creating more operational agility within tax—taking on routine compliance and reporting activities and freeing people to do more future-focused work. Generative AI solutions are likely to allow tax departments to unleash the power of data as structured and unstructured data sets will align closer to the point of transaction and flow through the financial systems with an emphasis on tax reporting. Taxability calculations, generation of tax documents – potentially customized to meet specific stakeholder and tax authority requirements, and impact analysis of new regulations may all be possible with generative AI solutions—enabling a more agile and dynamic tax department.

Investor relations (IR) can be complex and time-consuming with new challenges, expectations, and stakeholders. Generative AI models trained on company-specific data—including historical finance data, business unit leadership reports, new business and marketing content, prior IR materials, and public regulatory and economic content—could generate draft stakeholder materials such as period-over-period results, earnings call scripts, earnings releases, and competitive analyses, and could even potentially provide earnings Q&A draft narratives. Time spent gathering data and inputs can be redirected to focus on strategy and delivering a consistent and impactful message to stakeholders.