Moderated by Kate, Financial Technology Consultant at Hyperbots
Kate: First of all, thank you so much for joining us today. Let’s dive right into budgets and their integration with the chart of accounts. Can you start by explaining how budgets are typically managed in organizations? Are they usually a part of the ERP’s chart of accounts, or are they maintained separately?
Jon Naseath: Well, they’re typically separate. When you think of accounting, that’s looking backward, and the budget is looking forward. But then there are also budgets, and then there are forecasts, which are often different types of forecasts. When we’re talking about a budget, that’s usually over the current year or the next year in a monthly forecast. So, you could argue that it does align with the chart of accounts and could be managed similarly. I have managed a chart of accounts-level budgets and updated the budgets into the ERP system. But it’s not usually required. In some cases, it’s done by a separate tool outside of finance, more FP&A tools as opposed to the ERP main accounting system.
Kate: Moving on to the next question, how do revenue and cost heads from the COA in the ERP map to budget breakdowns in these FP&A groups?
Jon Naseath: Yep. So usually, you take your chart of accounts with all the different detailed accounts and levels. The end output is management reporting, which helps management make the right business decisions and see the impact of changes. Even though the chart of accounts might be more detailed, finance often has to translate and map accounts over to specific revenue or cost groupings for budget and management accounting.
Kate: Understood. What level of granularity is generally required for budgeting in industries like manufacturing?
Jon Naseath: The level of granularity depends on the decisions managers need to make for the business’s performance. In some cases, you may have too much detail, where it doesn’t impact decision-making, and in other cases, there’s not enough detail, which blinds you to what’s happening. Think of the chart of accounts as your general ledger for slicing and dicing data across the business, while sub-ledgers like accounts payable may provide specific insights without needing detail in the chart of accounts.
Kate: Moving forward, could you provide examples of budget heads for industries like construction, healthcare, clinical trials, and automobile dealerships?
Jon Naseath: At a summary level, if you think about what’s in a P&L, they’re similar. You have revenue, direct costs, and gross margin, but there are different standards for items like gross sales, net revenue, cost of goods sold, or cost of revenue in SaaS models. Below the cost of goods, you might also consider things like marketing or acquisition costs. Industry standards affect terminology but are mainly to make it easier for analysts to understand your business in comparison to others.
Kate: How can AI help maintain the integrity of budget heads or the structure within the COA?
Jon Naseath: Businesses are always changing, and it’s often challenging to update the chart of accounts in the ERP system. AI can assist by helping map the chart of accounts to forecasts, allowing real-time translation of legacy items into a forecast view. This ensures management can get the necessary budget or management reporting even as business lines or revenue streams evolve.
Kate: What are the common challenges organizations face in integrating budgets with the COA and ERP systems?
Jon Naseath: Consistency and accuracy are big challenges. When I was a director for an S&P 500 company, I produced budgets and rolled-up forecasts. Reconciliations between monthly management reporting and forecasted numbers were challenging, often because accounting might tag costs differently from the FP&A team’s forecast assumptions. This can cause discrepancies, requiring communication and sometimes manual updates.
Kate: That was really insightful. So, we have reached the end of our interview. This is the last question. How can organizations leverage AI to overcome these challenges?
Jon Naseath: I’ve seen multiple examples where AI manages real-time mapping. With some guidance on mapping, AI can respond to management’s needs by pulling relevant data and accounts to provide consistent views. Additionally, AI can flag changes, helping ensure accuracy in budgets and management reporting. AI is increasingly capable of assisting the companies I work with in maintaining this consistency.
Kate: That was very insightful. Thank you so much, Jon, for those insights. It’s clear that AI has a big role to play in the future of financial management. Thank you for joining us today.
Jon Naseath: Pleasure! Thank you.
Kate: Thank you.
Moderated by Emily, Digital Transformation Consultant at Hyperbots
Emily: Hello, everyone. This is Emily, a digital transformation consultant at Hyperbot Systems, and on the call, I’m really glad to have Anna with me. Anna is the CFO at Blend2Balance. In today’s discussion, we’ll be talking about a CFO’s preparatory toolkit for the adoption of AI. But before we dive in, Anna, would you share a brief overview of your background and perhaps set the stage for our discussion?
Anna: Sure. I’ve dedicated my entire career to finance, and I’ve been a CFO for more than 10 years. I’ve worked in various companies and industries. I started in steel manufacturing, spent around five years in pharmaceuticals, and joined an IT services company about four years ago. So I have a very versatile background in terms of industries. I also provide strategic consulting for early-stage startups. Since 2022, there’s been a huge emphasis on AI in all areas, including finance. Many organizations struggle to find the right approach to this transformative technology. It’s a pleasure to be here and shed some light on this crucial topic.
Emily: That’s really amazing. Great to have you as well, Anna. Let’s start with the first question. What would you recommend as the initial action for CFOs venturing into AI adoption?
Anna: I don’t recommend jumping into AI implementation initially. It’s worth running an audit in three main areas: data infrastructure, team skills, and the status of existing processes. For data infrastructure, it’s important to evaluate sources, ensure a single source of truth, address discrepancies, and prepare the data before implementing AI tools. Team readiness is paramount. Some teams are flexible with new technology, while others need more preparation to understand how it works. Lastly, the state of existing processes is vital. Are they unified and documented? Automating chaos leads to automated chaos, which is not what we want.
Emily: Completely agree. Those are insightful points, Anna. Moving forward, what key objectives would you recommend CFOs include in their AI strategic roadmap for the finance department?
Anna: When preparing the strategic roadmap for AI implementation, CFOs should focus on quantifiable objectives such as improving accuracy in financial forecasting, reducing processing times, and enhancing compliance and fraud detection. Setting a goal to automate 30% of manual data entry tasks within a year could significantly boost efficiency and accuracy. As a CFO, I’m always looking at the return on investment. AI implementation in finance operations should also consider potential savings and scalability if the organization plans to grow. Additionally, the cost of mistakes in finance operations is significant. AI can minimize errors, prevent fraud, and save the organization money in the long run.
Emily: True and valuable insights indeed. Considering your experience, what challenges should CFOs anticipate when aligning AI initiatives with their overall business strategy?
Anna: From what I’ve seen, security is a top concern among CFOs. Not understanding the technology can make it scary to let it make crucial decisions. Addressing security is crucial to reducing friction and gaining agreement from the rest of the team. I also recommend not rushing implementation. Let stakeholders adjust, understand the technology, and recognize its benefits to avoid big mistakes. In the long run, AI is a great technology. However, there’s pressure from leadership to implement it quickly to stay competitive. Finding the right balance between preparation and implementation and getting a leadership agreement is key.
Emily: Got it. Completely agree. Thank you so much, Anna, for sharing your insights and expertise on these critical aspects of adopting AI in finance. Any final thoughts or key takeaways you’d like to leave with our audience?
Anna: For CFOs feeling a bit lost in this process, I encourage them to do some reading or attend webinars. There’s a lot of information available, and it doesn’t take long to understand how the technology works and its benefits. Don’t be scared. It’s exciting to see changes in this market since finance automation tools haven’t seen a revolution since the 1970s.
Emily: That’s some great advice. Thank you so much, Anna, for being here and speaking on a topic that’s buzzing everywhere. It was truly amazing having you here today.
Anna: My pleasure.
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.
