Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Welcome to the latest installment of our interview series, where we delve into the intersection of finance and technology. Today, we are privileged to host Bimal Shah, an esteemed finance professional with extensive experience in the pharmaceutical industry, including serving as a CFO. Our focus for this session is on understanding the return on investment (ROI) of AI-led automation initiatives in finance. Let’s dive in!
Emily: Hello everyone, and welcome! I’m Emily, a digital transformation consultant at Hyperbots, and I’m thrilled to have Bimal joining us today. Bimal, before we jump into the details, could you please share a bit about your background?
Bimal Shah: Certainly, Emily. Thank you for having me. I’ve spent over a decade in senior financial roles within the life sciences industry, ranging from privately held firms to publicly traded companies. My expertise lies in navigating the complexities of finance in the pharmaceutical sector.
Emily: Thank you, Bimal, for that introduction. Let’s structure our discussion today into three key areas: understanding ROI methods, AI adoption in finance, and challenges and recommendations. Starting with ROI methods, Bimal, as a seasoned CFO, what frameworks have you employed to evaluate ROI?
Bimal: ROI, or return on investment, is paramount in financial decision-making. It can be measured through metrics such as internal rate of return, payback period, or simply as a ratio of investment returns. Assessing ROI involves considering factors like technology costs, implementation expenses, and potential cost savings or efficiency gains.
Emily: Fascinating insights, Bimal. Moving on to AI adoption in finance, which processes do you see as ripe for AI integration?
Bimal: Invoice processing, accounts payable, and accounts receivable management are prime candidates for AI adoption. These areas involve repetitive tasks that can benefit from automation, leading to cost savings and improved accuracy.
Emily: That’s insightful. And how would you prioritize AI adoption within the finance function?
Bimal: I would start with areas like accounts payable and receivable, where the tasks are relatively straightforward but labor-intensive. Demonstrating the benefits of AI in these areas can pave the way for adoption in more complex functions like financial planning and analysis.
Emily: Excellent advice, Bimal. Now, let’s delve into the nitty-gritty of calculating ROI. Could you elaborate on the quantitative and qualitative gains of AI-led automation?
Bimal: Quantitative gains include cost savings from reduced headcount and improved payment processing efficiency. On the qualitative side, benefits such as enhanced decision-making and employee satisfaction are harder to measure but equally valuable.
Emily: That’s a comprehensive overview. Bimal, how would you recommend measuring ROI for automation initiatives, considering both direct and indirect costs?
Bimal: Direct costs, such as technology investments and labor expenses, are relatively straightforward to quantify. However, capturing indirect costs and intangible benefits requires a more holistic approach. It’s essential to focus on measurable metrics while acknowledging qualitative gains.
Emily: Thank you for clarifying that, Bimal. As we near the end of our discussion, how would you suggest CFOs and controllers approach ROI measurement and publication for automation initiatives?
Bimal: I advocate for a balanced approach, emphasizing quantifiable benefits while acknowledging qualitative gains. Attempting to overly quantify intangible benefits may dilute the credibility of ROI calculations. Transparency and clarity are key when communicating the value of automation initiatives.
Emily: Wise counsel, Bimal. Finally, in terms of risk assessment, how do you recommend quantifying potential risks associated with AI implementation?
Bimal: While risks such as damaged relationships or employee concerns are challenging to quantify, they must be acknowledged and managed. Mitigating risks requires proactive communication, stakeholder engagement, and a focus on seamless implementation.
Emily: Thank you, Bimal, for your invaluable insights into maximizing ROI on AI-led automation initiatives in finance. It’s been a pleasure discussing these critical topics with you.
Bimal: Likewise, Emily. Thank you for hosting me, and I look forward to future conversations on the evolving landscape of finance and technology and there you have it, folks! A deep dive into the ROI of AI-led automation initiatives in finance, featuring insights from Bimal Shah, a seasoned CFO. Stay tuned for more enriching discussions on the intersection of finance and technology.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi everyone, good morning, good evening, or good afternoon depending on where you are. I am very pleased to have you back. The topic that we will be discussing today is large language models and their applications in accounting. But before we dive into it, Ayo, would you mind telling us a little more about yourself?
Ayo Fashina: Hi Emily, it’s good to see you again. My name is Ayo Fashina, I’m the CFO of Kobo360. It’s an e-logistics company startup, about five going on six years old now. We match goods owners to transporters and operate in seven African countries.
Emily: Got it. Thank you so much, Ayo, for the introduction. Today we’ll be talking about large language models (LLMs). It’s a vast topic in itself, but let’s start with the basics. Ayo, can you explain what large language models are and how they are relevant to the field of accounting?
Ayo Fashina: Thank you, Emily. In short, a large language model (LLM) is a type of artificial intelligence program that can recognize and generate text, among other tasks. LLMs are trained on huge datasets, hence the name large. They are built on machine learning, specifically a type of neural network called a transformer model. They analyze large datasets to learn what to look for when queried and use that knowledge to improve their performance. This makes them highly relevant to accounting, which involves data analysis and data collection.
Emily: Got it. Traditional methods of data analysis in accounting are human-based. Accounting staff or finance analysts do all the data entry and analysis, often using tools like Excel. How do LLMs compare to these traditional methods?
Ayo Fashina: One of the most significant benefits of LLMs is that they substantially increase the efficiency of accounting tasks. These models can process large volumes of data at an unprecedented speed, drastically reducing the time accountants spend on routine tasks such as data entry, transaction categorization, and report generation. Essentially, LLMs make the life of an accountant easier.
Emily: Understood. Can you dive into some specific applications of LLMs in accounting that you’ve come across or implemented?
Ayo Fashina: One application I’ve come across involves processing accounts payable. LLMs are trained on data from invoices, purchase orders, and delivery notes. They can match these documents to process accounts payable on behalf of accountants and even handle general ledger entries. Impressively, these models can read both typed and handwritten documents, which is a significant advancement.
Emily: How do LLMs contribute to improving efficiency, accuracy, and decision-making in accounting tasks?
Ayo Fashina: LLMs can process a lot of data very quickly and with high accuracy, thereby minimizing human errors. They ensure consistency in handling transactions and applying rules uniformly, which is crucial in accounting. By automating routine tasks, LLMs allow firms to allocate human resources to more strategic tasks, enhancing overall productivity. They also provide valuable insights for financial forecasting and decision-making by identifying trends and patterns that might be overlooked by human analysts.
Emily: What challenges do finance professionals face when implementing LLMs in accounting processes, and how can these challenges be addressed?
Ayo Fashina: Despite their advanced capabilities, LLMs lack human judgment and the ability to interpret complex and ambiguous financial situations. They operate based on the data provided to them without understanding the nuances of certain accounting decisions. Therefore, LLMs should be applied to tasks where human judgment is not a high priority. It’s also crucial to have robust data validation processes to ensure the quality of data used to train LLMs, as inaccurate data can lead to erroneous outputs.
Emily: What opportunities do LLMs present for innovation and advancement in accounting?
Ayo Fashina: LLMs hold great promise in revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. They can be used for risk assessment, fraud detection, and even forensic accounting. For example, LLMs can analyze data to uncover hidden patterns and trends, help identify risks, and flag anomalies in transactions, which aids in fraud prevention.
Emily: With the increasing use of LLMs in accounting, how do you ensure the security and privacy of sensitive financial data?
Ayo Fashina: Ensuring data confidentiality and security is paramount. Data hygiene is essential, meaning the data used to train LLMs should be sanitized of any personal identifiable information. Organizations should implement stringent data cleaning and sanitation procedures to remove sensitive information and identify potential biases and errors in the data.
Emily: How important is it for LLM-driven accounting solutions to integrate seamlessly with existing financial systems?
Ayo Fashina: It’s very important. If an LLM solution cannot integrate with existing systems, it defeats the purpose of having the LLM in the first place. Full integration ensures that the efficiency gains from LLMs are realized. Without it, the benefits are eroded by manual data transfer, which reintroduces human error.
Emily: What strategies do you recommend for ensuring smooth integration and compatibility with other accounting and enterprise systems?
Ayo Fashina: Testing compatibility ahead of full implementation is key. The provider of the LLM-driven solution should ensure seamless connection to existing systems and workflows. A sandbox test run before full implementation can help identify and resolve any integration issues.
Emily: What steps should organizations take to ensure compliance with relevant regulations while leveraging LLMs in accounting operations?
Ayo Fashina: Regulatory compliance is dynamic and always changing, which poses a challenge to LLMs. Ongoing monitoring and adjustments are required to keep LLMs compliant, which can be resource-intensive but necessary. Developers should ensure that LLMs are regularly updated to reflect changes in regulations.
Emily: Are there any emerging trends or advancements in LLMs that you believe will shape the future of accounting and finance?
Ayo Fashina: LLMs have the potential to revolutionize the finance sector in numerous ways. They can be used for risk assessment, fraud detection, and forensic accounting. LLMs can also simplify audit processes by organizing data for easier transaction tracing. As these technologies evolve, they will continue to provide valuable insights and efficiencies.
Emily: Based on your experience and insights, do you have any additional advice or recommendations for financial professionals looking to harness the power of LLMs?
Ayo Fashina: Financial professionals should consider learning Python, a versatile programming language that many LLMs are based on. Understanding Python fundamentals can help professionals leverage LLMs for code generation and task automation. As LLMs become more prevalent, the demand for Python skills will grow, making it a valuable skill for finance and accounting professionals.
Emily: Thank you so much, Ayo, for sharing your expertise on the applications of large language models in accounting. Is there anything else you would like to add before we conclude our discussion?
Ayo Fashina: Just to summarize, LLMs hold great promise for revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. However, it’s crucial to be aware of their limitations and the need for human judgment. Data security and regulatory compliance are also important considerations. Thank you, Emily, for having me. It’s been a pleasure.
Emily: Thank you, Ayo. It was great having you, and this discussion was truly insightful.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hi everyone, good morning, good evening, or good afternoon depending on where you are. I am very pleased to have you back. The topic that we will be discussing today is large language models and their applications in accounting. But before we dive into it, Ayo, would you mind telling us a little more about yourself?
Ayo Fashina: Hi Emily, it’s good to see you again. My name is Ayo Fashina, I’m the CFO of Kobo360. It’s an e-logistics company startup, about five going on six years old now. We match goods owners to transporters and operate in seven African countries.
Emily: Got it. Thank you so much, Ayo, for the introduction. Today we’ll be talking about large language models (LLMs). It’s a vast topic in itself, but let’s start with the basics. Ayo, can you explain what large language models are and how they are relevant to the field of accounting?
Ayo Fashina: Thank you, Emily. In short, a large language model (LLM) is a type of artificial intelligence program that can recognize and generate text, among other tasks. LLMs are trained on huge datasets, hence the name large. They are built on machine learning, specifically a type of neural network called a transformer model. They analyze large datasets to learn what to look for when queried and use that knowledge to improve their performance. This makes them highly relevant to accounting, which involves data analysis and data collection.
Emily: Got it. Traditional methods of data analysis in accounting are human-based. Accounting staff or finance analysts do all the data entry and analysis, often using tools like Excel. How do LLMs compare to these traditional methods?
Ayo Fashina: One of the most significant benefits of LLMs is that they substantially increase the efficiency of accounting tasks. These models can process large volumes of data at an unprecedented speed, drastically reducing the time accountants spend on routine tasks such as data entry, transaction categorization, and report generation. Essentially, LLMs make the life of an accountant easier.
Emily: Understood. Can you dive into some specific applications of LLMs in accounting that you’ve come across or implemented?
Ayo Fashina: One application I’ve come across involves processing accounts payable. LLMs are trained on data from invoices, purchase orders, and delivery notes. They can match these documents to process accounts payable on behalf of accountants and even handle general ledger entries. Impressively, these models can read both typed and handwritten documents, which is a significant advancement.
Emily: How do LLMs contribute to improving efficiency, accuracy, and decision-making in accounting tasks?
Ayo Fashina: LLMs can process a lot of data very quickly and with high accuracy, thereby minimizing human errors. They ensure consistency in handling transactions and applying rules uniformly, which is crucial in accounting. By automating routine tasks, LLMs allow firms to allocate human resources to more strategic tasks, enhancing overall productivity. They also provide valuable insights for financial forecasting and decision-making by identifying trends and patterns that might be overlooked by human analysts.
Emily: What challenges do finance professionals face when implementing LLMs in accounting processes, and how can these challenges be addressed?
Ayo Fashina: Despite their advanced capabilities, LLMs lack human judgment and the ability to interpret complex and ambiguous financial situations. They operate based on the data provided to them without understanding the nuances of certain accounting decisions. Therefore, LLMs should be applied to tasks where human judgment is not a high priority. It’s also crucial to have robust data validation processes to ensure the quality of data used to train LLMs, as inaccurate data can lead to erroneous outputs.
Emily: What opportunities do LLMs present for innovation and advancement in accounting?
Ayo Fashina: LLMs hold great promise in revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. They can be used for risk assessment, fraud detection, and even forensic accounting. For example, LLMs can analyze data to uncover hidden patterns and trends, help identify risks, and flag anomalies in transactions, which aids in fraud prevention.
Emily: With the increasing use of LLMs in accounting, how do you ensure the security and privacy of sensitive financial data?
Ayo Fashina: Ensuring data confidentiality and security is paramount. Data hygiene is essential, meaning the data used to train LLMs should be sanitized of any personal identifiable information. Organizations should implement stringent data cleaning and sanitation procedures to remove sensitive information and identify potential biases and errors in the data.
Emily: How important is it for LLM-driven accounting solutions to integrate seamlessly with existing financial systems?
Ayo Fashina: It’s very important. If an LLM solution cannot integrate with existing systems, it defeats the purpose of having the LLM in the first place. Full integration ensures that the efficiency gains from LLMs are realized. Without it, the benefits are eroded by manual data transfer, which reintroduces human error.
Emily: What strategies do you recommend for ensuring smooth integration and compatibility with other accounting and enterprise systems?
Ayo Fashina: Testing compatibility ahead of full implementation is key. The provider of the LLM-driven solution should ensure seamless connection to existing systems and workflows. A sandbox test run before full implementation can help identify and resolve any integration issues.
Emily: What steps should organizations take to ensure compliance with relevant regulations while leveraging LLMs in accounting operations?
Ayo Fashina: Regulatory compliance is dynamic and always changing, which poses a challenge to LLMs. Ongoing monitoring and adjustments are required to keep LLMs compliant, which can be resource-intensive but necessary. Developers should ensure that LLMs are regularly updated to reflect changes in regulations.
Emily: Are there any emerging trends or advancements in LLMs that you believe will shape the future of accounting and finance?
Ayo Fashina: LLMs have the potential to revolutionize the finance sector in numerous ways. They can be used for risk assessment, fraud detection, and forensic accounting. LLMs can also simplify audit processes by organizing data for easier transaction tracing. As these technologies evolve, they will continue to provide valuable insights and efficiencies.
Emily: Based on your experience and insights, do you have any additional advice or recommendations for financial professionals looking to harness the power of LLMs?
Ayo Fashina: Financial professionals should consider learning Python, a versatile programming language that many LLMs are based on. Understanding Python fundamentals can help professionals leverage LLMs for code generation and task automation. As LLMs become more prevalent, the demand for Python skills will grow, making it a valuable skill for finance and accounting professionals.
Emily: Thank you so much, Ayo, for sharing your expertise on the applications of large language models in accounting. Is there anything else you would like to add before we conclude our discussion?
Ayo Fashina: Just to summarize, LLMs hold great promise for revolutionizing accounting and finance by automating routine tasks, enhancing efficiency, and providing valuable insights. However, it’s crucial to be aware of their limitations and the need for human judgment. Data security and regulatory compliance are also important considerations. Thank you, Emily, for having me. It’s been a pleasure.
Emily: Thank you, Ayo. It was great having you, and this discussion was truly insightful.
Moderated by Emily ,Digital Transformation Consultant at Hyperbots
Emily: Hello, everyone. This is Emily, and I’m a digital transformation consultant at Hyperbots. Good morning, good afternoon, or good evening, depending on where you are. Today, we delve into the fascinating intersection of artificial intelligence and the realm of certified public accountants. Specifically, we aim to explore whether AI has the capability to fully acquire CPA knowledge and its impact on the ease of audit processes. Joining us with this enlightening discussion is Cecy, a distinguished CPA with extensive expertise in finance and auditing.
Cecy: Thank you for having me, Emily. I’m excited to be part of this conversation.
Emily: To get things off, let’s discuss the role of AI in finance, Cecy. Could you share your insights on how AI automation can influence financial processes within any organization?
Cecy: Absolutely. So AI can be truly transformative, especially around automating repetitive and time-consuming tasks. For instance, AI-powered tools can streamline transaction processing and risk assessment, and even financial forecasting. These tools can analyze vast datasets much more quickly and accurately than human teams could, freeing up our staff to focus on more strategic and analytical tasks. This shift will not only improve efficiency but also enhance our ability to make informed financial decisions.
Emily: That’s fascinating. So, Cecy, can you provide examples of how AI can successfully integrate into financial auditing procedures?
Cecy: Sure. One notable example is the use of AI in enhancing the precision of audit processes. AI algorithms can be employed to analyze transactional data and identify anomalies or patterns that might indicate errors or fraud. This capability allows our auditors to focus their efforts on higher risk areas, significantly improving audit quality. Additionally, AI tools can read and interpret complex contracts or financial statements, making the audit process faster and reducing the likelihood of human error.
Emily: So, Cecy, there’s a common question looming. Can all aspects of CPA knowledge be replicated or replaced by AI algorithms? I just want to understand your take on it.
Cecy: This is a really intriguing question. While I believe that AI can replicate many aspects of CPA knowledge, especially around data processing and pattern recognition, it can’t fully replace the professional judgment and ethical considerations that CPAs bring to their work. The interpretation of complex financial regulations, decision-making in ambiguous situations, and ethical considerations in auditing are all areas where human judgment remains indispensable. Therefore, AI serves more as a powerful tool that complements the expertise of CPAs rather than replacing them entirely.
Emily: That’s definitely an important distinction. So, Cecy, when the time comes, how will your organization ensure that the expertise of CPAs is effectively integrated with AI technologies?
Cecy: Our approach is centered around continuous training and collaboration throughout the whole organization. We’re invested in upskilling our CPAs so that they can work effectively with AI technologies, ensuring that they understand how to leverage these tools to enhance their work. This includes training on interpreting AI-generated insights and integrating those findings into audit processes. Moreover, we’ll foster a collaborative environment where AI developers and CPAs work closely to tailor AI solutions to meet the unique needs of our financial auditing processes, ensuring synergy where human expertise and machine efficiency are working together.
Emily: So, Cecy, let’s delve deeper into the correlation between AI automation and the ease of audit processes. How do you perceive this relationship?
Cecy: I perceive this relationship as undoubtedly synergistic. AI automation can significantly ease audit processes by handling the heavy lifting of data analysis, which is a cornerstone of auditing. This allows our auditors to allocate more time to scrutinizing complex issues, strategic planning, and advising clients. The integration of AI will lead to audits that are not only more efficient but also more comprehensive, as AI can uncover insights that might be overlooked by human auditors.
Emily: That’s a nuanced perspective. So, Cecy, what significant improvements can one expect in audit efficiency post-adopting AI technologies?
Cecy: I expect the adoption of AI technologies to lead to substantial improvements in audit efficiency and accuracy. For example, AI’s ability to process and analyze large volumes of data in real-time will shorten the audit cycle, allowing us to deliver insights to our clients faster. Additionally, AI’s predictive capabilities can enhance our risk assessment processes, enabling us to identify and mitigate potential issues early in the audit process.
Emily: So, maintaining data quality and integrity is paramount in auditing. How will your organization address potential biases or inaccuracies that may arise from relying on AI algorithms?
Cecy: Addressing biases and inaccuracies is critical for us. We must implement rigorous testing and validation procedures for our AI algorithms to ensure accuracy and unbiased results. This includes regular audits of the algorithms themselves and their outputs conducted by both AI specialists and CPAs. Furthermore, we need to emphasize the importance of diversity in teams developing and overseeing AI tools, as diverse perspectives help identify and mitigate potential biases in AI algorithms.
Emily: So, Cecy, how do you evaluate the return on investment of implementing AI technologies in auditing?
Cecy: Evaluating the ROI on implementing AI technologies involves assessing both quantitative and qualitative benefits. Quantitatively, we look at metrics like reductions in audit time, improvements in error detection rates, and cost savings from streamlined processes. Qualitatively, we assess improvements in audit quality, client satisfaction, and the ability to offer more strategic insights. The combined analysis of these factors helps us understand the value AI brings to our auditing services.
Emily: What advice would you offer to organizations considering investing in AI for auditing purposes?
Cecy: My advice would be to start with a clear strategy aligned with your organization’s specific needs and challenges. It’s essential to invest in both technology and team training to work effectively with AI. Building a culture of innovation and continuous learning can significantly enhance the integration of AI into auditing processes. Moreover, it’s important to prioritize transparency and ethical considerations in the deployment of AI technologies to ensure everyone understands the benefits.
Emily: From a future outlook standpoint, Cecy, how do you envision the role of AI evolving in finance and auditing in the coming years?
Cecy: In the coming years, AI will become even more integrated into finance and auditing, driving further innovations and efficiencies. We will see AI being used more creatively, providing strategic insights beyond just improving efficiencies and accuracy in audits. The evolution of AI will also prompt a shift in the skill sets required by finance and auditing professionals, emphasizing more analytical and strategic thinking. Ultimately, the role of AI will continue to evolve and offer more exciting possibilities for enhancing the value and impact of our financial services.
Emily: Thank you so much, Cecy, for your invaluable insights. Your perspective on the correlation between AI automation and the ease of audit processes has been enlightening.
Cecy: It’s been my pleasure. Thank you for facilitating this discussion.
Emily: As we conclude our exploration of whether all CPA knowledge can be acquired by AI, it’s evident that AI automation is a powerful tool that can greatly enhance auditing processes. By leveraging AI effectively and addressing potential challenges, organizations can unlock the full potential of AI in auditing and achieve greater efficiency and accuracy in financial processes.