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 Moderated by Niyati Chhaya, Co-founder at Hyperbots
Niyati Chhaya: Hi everyone, good morning, good evening, and good afternoon. This is Niyati Chhaya. I am a co-founder and I lead AI at Hyperbots. I am thrilled to have Mike with us today. Mike Vaishnav is a CFO, Consultant, and Strategic Advisor to many privately owned organizations. We’re going to pick his brains on AI and compliance. But before that, Mike, why don’t you introduce yourself?
Mike Vaishnav: Thank you, Niyati, it’s a pleasure to be here. I’ve been working in Silicon Valley for about close to 30 years in diverse industries. I’ve had the opportunity to touch each and every aspect of finance, from the controllership role to FP&A, treasury, tax, investor relations, and more. In my last two roles, I also managed operations departments like HR, IT, facilities, legal, and procurement. So, I bring a broad range of experience in finance and operations.
Niyati Chhaya: Wow, I think you are the right person to talk about finance, compliance, and how AI will help in compliance. How do you think AI will assist in compliance?
Mike Vaishnav: AI can significantly enhance compliance. With the right algorithms, AI can flag issues related to government and regulatory requirements. By identifying violations early, companies can address problems before they escalate. AI helps ensure that businesses are operating within the bounds of policies and regulations.
Niyati Chhaya: Got it, and I assume AI can help with fraud detection as well?
Mike Vaishnav: Absolutely, fraud detection is a critical area where AI can be very effective. By monitoring data and raising flags at appropriate levels, AI can help mitigate fraud and ensure compliance with regulatory requirements.
Niyati Chhaya: Do you think AI can play a role in the audit process?
Mike Vaishnav: Definitely. AI can analyze vast amounts of data and identify specific patterns and exceptions. For example, AI can track journal entry approvals, identifying who processed and approved entries. This can streamline the audit process, allowing auditors to focus on more value-added functions rather than spending time on sample tests and data checks.
Niyati Chhaya: Do you think AI can ensure the accuracy of financial reporting?
Mike Vaishnav: Yes, AI can enhance the accuracy of financial reporting. While ERP systems are robust, there are instances where information might be missed. AI can identify these gaps early by using predefined rules and algorithms. For example, when generating financial statements, AI can ensure that all relevant chart accounts are included, reducing the risk of errors. Additionally, AI can assist in compiling and analyzing data for SEC filings, providing insights that ERP systems might not offer.
Niyati Chhaya: Thanks for those insights. My takeaway here is that building reliable AI systems can greatly benefit compliance processes.
Mike Vaishnav: Absolutely. Compliance is crucial for all finance professionals. With AI ensuring compliance, finance teams can rest easy, knowing that they have a reliable system monitoring their processes. AI provides detailed analytics, helping to maintain and improve compliance.
Niyati Chhaya: Got it. Thank you so much, Mike, for sharing your expertise and insights on AI and compliance.
Mike Vaishnav: My pleasure.