AI promises specific benefits for business accounting and auditing. It can be used to:
- Implement greater internal controls;
- Conduct cost and variance analysis in management control systems;
- Execute continuous auditing and generate real-time financial information;
- Provide decision support;
- Conduct risk assessment;
- Classify transactions for control accounting;
- Systematically and automatically review documents to detect anomalies and fraud;
- Automate rules-based, repetitive and high-volume work; and more.
Broader application of AI in the accounting and auditing professions is expected to:
- increase efficiency, productivity and accuracy,
- accelerate and improve decision making,
- reduce accounting costs, and
- free accounting staff from low-level activities to focus on higher-value tasks, in spite of the challenges of a technologically unskilled workforce.
Generative AI in Accounting
Generative AI in particular has the potential to help the accounting function in several ways. These large language model (LLM) forms of AI could add substantial value to the accounting and finance worlds through automated report generation, fraud detection, demand / revenue forecasting, automation of routine tasks and improved accuracy.
In its present form, however, its practical application in finance is significantly limited for different reasons including the lack of transparency and regulatory compliance.
Among the newcomers to the world of digital financial intelligence at the service of business is the recently launched generative AI app from Bloomberg, one of the world’s largest financial and economic information groups.
BloombergGPT, designed, built from scratch and trained expressly for the world of finance by Bloomberg, 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.
BloombergGPT: the ChatGPT of finance
BloombergGPT comes from a set of algorithm models that are based on huge numbers (in the billions) of parameters ー in this case, there are 50 billion.
The company specifically trained its LLM to support a diverse set of natural language processing (NLP) tasks within the financial industry to help the company enhance its existing financial NLP-based services, such as
- sentiment analysis
- name and entity recognition,
- news classification and
- question answering.
In essence, BloombergGPT will marshall the vast quantities of data on the Bloomberg terminal, unlocking new opportunities to better help the company’s customers, while bringing the full potential of artificial intelligence-driven tools into the financial domain.
Caution is warranted, however, before embarking on the rapid and full-scale deployment of an LLM-based AI tool in your financial department. So far, in these very early days of experimentation with and use of generative AI models, the following negative aspects have emerged:
- The ability for bad actors to use it to create more sophisticated cyber attacks.
- The huge quantity of data needed and the related privacy issues.
- The substantial number of biases identified so far that have yet to be corrected.
- The generation of inaccurate or false information and misinformation
- Inability to give explanations regarding investments.
- The recent discovery that humans easily give complete credence to output generated by such AI models and consequently suspend their own critical faculties.
Regulatory and standards-setting organizations in the accounting field must consider the potential impact of these new technologies on the financial, accounting and auditing sectors in general, on financial reporting standards in particular, and the importance of the transparency of the data and sources used and then produced by machine learning models.
While they should encourage the adoption of smart technologies in accounting and auditing practice, they should also emphasize the importance of increasing the finance function’s knowledge about AI, its potential applications, and the associated risks.
Compliance requirements and regulations for finance and taxation will inevitably continue to evolve rapidly. AI applications promise to become powerful allies to financial departments in this regard ー but only if they can be shown to be reliable and trustworthy.
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