Generative AI has the potential to help the accounting function in several ways.
ChatGPT, a large language model (LLM) form of AI, could add substantial value to the accounting and finance worlds, although it has some significant limitations for practical use in finance in its present form.
BloombergGPT, designed, built from scratch and trained expressly for the world of finance by Bloomberg, one of the world’s largest financial and economic information groups, is one example of a ChatGPT-like, purpose-made application for finance. It can be used by Bloomberg terminal subscribers to more rapidly find, query and analyse relevant data before making investment decisions.
The main benefits of generative AI in the financial function include:
Automated report generation
Generative artificial intelligence could be used to automate both the generation of financial reports and the forecasting of collections.
On the one hand, by accessing the appropriate data, it could rapidly generate balance sheets and profit-and-loss accounts; on the other hand, future invoice payments could be more accurately predicted based on historical data.
Artificial intelligence models could thus be used to trigger proactive collection actions before payments become due.
These predictions would allow collections staff to focus their efforts on higher-risk accounts. Such collection forecasts would also contribute to the overall ML-based cash flow forecasts.
This would save CFOs time and reduce the risk of the errors associated with manual report generation.
In previous articles, we have seen how generative AI can be used to make fraud and cyber attacks more technically precise and effective (as in phishing).
On the other hand, however, it can also be used to detect anomalies in financial data that could indicate fraud.
By analyzing large volumes of data, generative AI can identify patterns and trends that might elude human auditors.
Demand / revenue forecasting
Using internal and external data sources, generative AI can be used to predict financial trends and identify potential risks and opportunities.
This could help companies make more informed decisions on budgets, investments and other financial activities.
Percentage of completion (POC) forecasting is also possible: ML models can predict percentage of completion metrics (e.g. hours, costs, units, weight, etc.) to predict the total remaining completion effort.
Automation of routine tasks
Automating routine accounting tasks, such as data entry and invoice processing, frees up accountants’ time to focus on more strategic and high-value activities, such as financial analysis, planning and strategy.
Since anomaly and error detection can also be automated, it would become easier to comply with accounting and tax laws and requirements because erroneous transactions or balances would be flagged and highlighted by the system.
Real-time analysis during data entry would prevent errors from filtering through the workflow, avoiding costly downstream corrections.
Generative artificial intelligence could help improve the accuracy of accounting data by detecting anomalies and inconsistencies in financial records.
This would help ensure the reliability and accuracy of financial statements, which would support better financial decision-making.
Moreover, scenario modelling using hypothetical data could help CFOs predict the potential outcomes of decisions that have never been made before.