Impact Of Prompt Engineering On Accountants And Auditors
Benefits And Challenges
BY DOMINIQUE TAN
16 Jan 2024
TAKEAWAYS
Prompt engineering can benefit accounting and finance professionals in different ways, such as improving efficiency and accuracy, data analysis and regulatory compliance, and risk assessment and fraud detection. It also helps in supporting strategic decision making and professional learning and development.
Prompt engineering poses challenges as well, including bias and misinterpretation, overreliance on AI, and security and privacy concerns.
There must be careful consideration when working with AI-generated outputs.
Artificial intelligence is having a profound impact on the accounting and finance industry and is also revolutionising how products and services are being built and implemented. In accounting and finance, labour-intensive and time-consuming tasks such as audits, banking, tax preparation, bookkeeping, reporting, and payroll have been successfully automated. With the integration of emerging technologies into accounting, self-learning systems will carry out repetitive and time-consuming tasks, leaving humans to perform analytical and managerial tasks. By using robotic process automation (RPA), for example, documents and contracts can be processed in weeks rather than months.
In the context of accountants and auditors, prompt engineering can bring real benefits:
Improved efficiency: Prompt engineering can help accountants and auditors quickly obtain relevant information – including retrieving pertinent information from vast amounts of data, such as relevant accounting standards, regulations, and financial documentation – or perform specific tasks. By providing well-crafted prompts, they can receive more accurate and focused responses, reducing the time spent on research and data gathering.
Support for decision making: Well-structured prompts can help professionals access relevant data and insights quickly, enabling informed decision making in areas such as financial planning, investment analysis, and risk management.
Invisible accounting: Accountants can gain a clearer understanding of behind-the-scenes operations, much of which is automated. The insights from this “invisible accounting” contribute to their strategic decision making.
Continuous auditing: As auditing is ongoing without gaps or breaks, accounting managers and leaders have real-time visibility into their financials. Financials are timely and correct with minimal errors (compared to those using sample datasets or done by humans).
Improved accuracy: Carefully crafted prompts can help ensure that natural language processing (NLP) models provide accurate and precise responses. This can be particularly valuable when performing complex calculations or verifying financial data, reducing the risk of errors in financial reporting and auditing.
Enhanced data analysis: AI language models can assist accountants and auditors in analysing financial data by interpreting complex information and generating insights. Properly designed prompts can trigger the model to provide relevant analysis or identify patterns, anomalies or trends in financial statements, enhancing decision-making processes.
Risk assessment and fraud detection: With the help of AI language models, auditors can analyse financial transactions and detect potential fraud more effectively. By leveraging prompt engineering, auditors can elicit responses tailored to identifying irregularities and suspicious activities in financial data. NLP models with well-designed prompts can help identify financial risks and anomalies in large datasets, facilitating more effective risk assessment and mitigation strategies.
Digitalisation: The digitalisation of a file provides a clear picture of who accessed it, when (date and time), and from where. In an audit, auditors do not have to dig through file cabinets for paper documentation since digital files are readily available. By carrying out audits this way, the accuracy and efficiency of audits are maximised, and it is much easier to audit all financial transactions for a company rather than just a sample.
Financial reporting and auditing: Accountants can use prompt engineering to generate comprehensive and accurate financial reports. By specifying the desired format and content, they can receive outputs that comply with relevant accounting standards, reducing the risk of errors in the financial statements. NLP models can generate standardised and consistent audit documentation, ensuring that all necessary information is captured and organised effectively throughout the auditing process.
Regulatory compliance: AI language models can assist in keeping track of complex accounting and auditing regulations. By using appropriately designed prompts, accountants and auditors can receive up-to-date information on changes in regulations and compliance requirements.
Training and professional development: Prompt engineering can also be used to create training materials for accountants and auditors. AI language models can generate interactive learning content, practice questions and explanations, aiding in professional development and continuous learning. Accountants and auditors can stay current with industry trends and enhance their knowledge and skills.
Prompt engineering has many other value propositions, including:
Enhanced communication: Prompt engineering can assist in generating clear and concise written communication, whether in client correspondence, audit reports, or internal memos. This can improve the clarity of financial information and recommendations, reducing misunderstandings and potential disputes.
Customisation: Accountants and auditors can tailor prompts to specific client needs, industry requirements, or regulatory frameworks. This flexibility enables professionals to use NLP models to address unique challenges and adapt to changing circumstances.
Multilingual support: For accountants and auditors working in international contexts, prompt engineering can enable NLP models to provide assistance and insights in multiple languages, breaking down language barriers in global financial operations.
Enhanced audit quality: By automating routine procedures and improving data analysis, prompt engineering can contribute to the overall quality and thoroughness of audits, reducing the likelihood of material misstatements going unnoticed.
Ethical considerations: Carefully designed prompts can also help accountants and auditors navigate ethical dilemmas by promoting responsible and ethical AI usage, safeguarding client confidentiality and privacy.
Prompt engineering can empower accountants and auditors to work more efficiently, accurately, and effectively in a rapidly changing financial and regulatory landscape. It complements their expertise with the capabilities of NLP models, helping them provide valuable insights, ensure compliance, and deliver high-quality financial services to clients and stakeholders.
CHALLENGES
However, it is crucial to recognise the potential challenges and risks associated with prompt engineering, in the context of accountants and auditors:
Bias and misinterpretation: If prompts are not carefully crafted, AI language models might generate biased or inaccurate responses. This can lead to incorrect financial analysis, decision making, or compliance-related issues.
Overreliance on AI: While AI can enhance productivity, relying too much on language models for critical tasks may result in a loss of human expertise and judgement. It is important to use AI as a tool alongside human judgement and understanding.
Security and privacy concerns: Using AI language models for sensitive financial data requires robust security measures to safeguard against potential breaches or unauthorised access.
CONCLUSION
Prompt engineering can significantly impact accountants and auditors by improving efficiency, data analysis, risk assessment, and compliance. It is no longer sufficient for accountants and auditors to know what questions to ask; it is becoming ever more important to know how to ask the right questions, in order to get the desired output.
However, careful consideration should be given to potential biases and risks associated with AI-generated outputs, and professionals should exercise caution and expertise while utilising these language models in their work.
Dominique Tan is Country Leader – Singapore, KNAV Services LLP.
