Impact of GL coding on financial reporting and audits

Find out interesting insights with John Silverstein, VP of FP&A at Extreme Reach

Moderated by Emily, Digital transformation consultant at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

Emily: Hi, everyone. This is Emily. I’m a digital transformation consultant at Hyperbot Sync, and I’m pleased to have John, who is the VP of FP&A at Extreme Reach, on the call with me. So thank you so much for joining us today, John, really appreciate it.

John Silverstein: No problem. Thank you for having me.

Emily: Of course. So, John, the topic that we’d be discussing today is the impact of GL coding on financial reporting and auditing and I want to begin things by asking why GL coding is so important for financial reporting and auditing.

John Silverstein: Yeah, GL coding is critical for your financial reporting and audit, otherwise it becomes very difficult to report correctly, and you have to go to the transaction level, and then it defeats the purpose of the GLs. So it’s critical. It’s also extremely hard to audit something when it’s mixed, and you’re also generally not compliant if you’re not coding things properly in the financials. So it is critical both from a reporting and audit perspective and also for management to make proper decisions and everything to make sure that selling and marketing expenses are selling and marketing expenses and then it’s easily tracked. And you know who the owners are, and you know what it is.

Emily: Got it. So let me ask you this, how does a well-structured GL coding scheme enhance decision-making for the management?

John Silverstein: Yeah, if you well structure the GL coding, the decision-making, you have to align your GL coding to not only align from an accounting and GAAP standpoint, but also to make sure that you can make proper decisions on pricing or do proper ratios and things like that based on how you break your financials out. GL coding allows you to get segment reporting and figure out how profitable certain areas, products, or service lines are. If you don’t break it out and you don’t have the right granularity, it gets complicated to figure out the important information to make decisions in the business.

Emily: Got it. So just out of curiosity, John, can you provide an example of how GL coding affects compliance with regulatory and tax requirements?

John Silverstein: Yeah, GL coding is critical. If your transactions aren’t accurately classified with GAAP, it can affect your tax calculations, anything from sales tax to VAT to corporate income tax. You can end up overpaying or facing audit issues on the tax side as well. You must follow proper accounting, have the right GL coding, and minimize penalties for noncompliance. If VAT is consistently coded under a specific GL account, it becomes much easier to prepare accurate VAT returns and comply with local tax authorities.

Emily: Got it. So what are some of the common mistakes that organizations make with their GL coding schemes, and how can they be avoided?

John Silverstein: The biggest mistake is over-complication of the GL, where you start making GL codes for everything. But then there’s the opposite side, where you don’t break things out at all, and it’s all lump amounts, which makes decision-making hard. It’s critical to find a balance between detail and summary-level data. Make sure you have proper hierarchies so you can go more granular if needed and have a proper roll-up. There are also tools now that help with different ways of reporting from a management perspective that can help this as well.

Emily: Understood. Also, John, how can a GL coding scheme be designed to provide real-time or near-real-time financial reporting?

John Silverstein: If your GL coding is proper, then as transactions are happening in your GL, ERP, or even CRM, you can see that data at the right levels in near real-time. This allows you to see where you might end up from a financial standpoint and make decisions like ramping up production or slowing down sales based on live data. It’s important to align GL coding with ERP, CRM, and procurement systems to get live financial analysis instead of waiting for month-end close.

Emily: Got it. John, would you provide an example of how GL coding alignment with business strategy can improve performance monitoring?

John Silverstein: Sure. If a company aligns its GL coding schema with key performance indicators, it can monitor and optimize these metrics more effectively. For instance, a SaaS company might use specific GL codes for different components of customer acquisition costs and retention expenses, which gives insights into performance against goals. By doing this, you can generate financial reports focusing on metrics like customer acquisition costs, helping to make more strategic decisions in real time.

Emily: Makes sense. So a little bit about the audit process, John. How does a well-structured GL coding scheme simplify the audit process?

John Silverstein: A well-structured GL coding scheme simplifies the audit process by providing a clear and consistent trail of the transactions. This allows auditors to quickly trace entries, verify accuracy, and ensure compliance with accounting standards. For example, if an organization uses separate GL codes for office supplies at HQ and regional offices, auditors can efficiently sample and analyze expenses related to different locations. However, you need to be careful to ensure your schema isn’t overly complicated.

Emily: Got it. Just to wind things up, one last question, John. What would be your key recommendations for organizations looking to optimize their GL coding scheme?

John Silverstein: First, design a hierarchical structure that goes down to a detailed level but allows summarization. Use multi-dimensional analysis so you can get different insights like company roll-ups, cost centers, departments, and product lines without mixing everything into the GL. Balance granularity and simplicity, and align with the business strategy because strategies evolve. It’s important that GL coding reflects the current business direction. Integrate with other systems like ERP to avoid silos, and regularly review your coding schema to ensure it complies with current regulations and organizational structures.

Emily: Got it. Thank you so much, John, for sharing your insights on the critical role of GL coding in financial reporting, auditing, and decision-making. Your examples and recommendations will certainly help organizations better structure their GL coding schemes to achieve more actionable financial reports. Thank you so much for being here.

John Silverstein: No problem. Glad to be here.

AI, friend or foe to finance?

Find out interesting insights with Mike Vaishnav, CFO & Strategic Advisor

Moderated by Emily, Digital Transformation Consultant at Hyperbots

Don’t want to watch a video? Read the interview transcript below.

Emily: Hi everyone. Good morning, good afternoon, good evening, depending on where you are. I’m Emily, a digital transformation consultant at Hyperbot Systems, and I’m very pleased to have Mike Vaishnav on the call with me. Mike is a CFO, consultant, and strategic advisor to various privately-held organizations. Before we get started on our discussion on how AI can be a friend rather than a foe to companies, Mike, could you tell us a little more about yourself?

Mike Vaishnav: Of course, thank you, Emily. I’ve been working in Silicon Valley for close to 30 years in various roles, ranging from controllership to FP&A, treasury, tax, significant M&A transactions, and process improvement system implementations. I’ve worked with companies of different sizes, from $60 million to $22 billion. In my last two roles as a CFO, I also managed HR, legal, and IT functions. So, that’s my overall background. Let’s focus on our topic rather than my background.

Emily: Thank you so much for the introduction, Mike. Today’s discussion will cover three broad categories: technology evolution in finance, the perceived threats of AI, and the benefits of AI. Starting with technology evolution, Mike, as you mentioned, you’ve spearheaded different finance functions in various organizations of varying sizes. Would you like to briefly share your key experiences?

Mike Vaishnav: Of course. I’ve seen technology evolve from mainframe computers in the early ’90s to the latest cloud-based technology. The speed and analysis of data have changed significantly. Automation and process improvements have been tremendous. We’re now entering a stage where AI can further evolve technology, especially in the finance industry.

Emily: You’ve been part of different waves of technology in finance, from manual bookkeeping to advanced ERP systems. What technological evolution have you seen over the years?

Mike Vaishnav: Automation has progressed from manual processes to cloud-based systems. Adding AI and other solutions to existing ERP systems can automate processes and make finance functions more efficient and effective.

Emily: These days, there’s a lot of buzz around AI. How do you see AI affecting the finance function?

Mike Vaishnav: AI can significantly enhance the finance function. AI is essentially human intelligence on a computer, helping finance take the next step. AI can gather and analyze large amounts of data, complementing human efforts. It can provide real-time, accurate data, improving decision-making and operational efficiency. AI can help finance executives focus on detailed analysis to improve profitability and efficiency.

Emily: Thank you, Mike. In the next part, we will discuss the potential threats of AI.

Emily: Welcome back, Mike. Here, we’ll talk about the threats of AI. AI is seen as a threat by some and a friend to others. Why are the perceptions so different?

Mike Vaishnav: People see AI as a threat mainly due to fears of job losses, data security, and privacy issues. There’s also a concern about people becoming too reliant on AI and potential biases in data. Since AI is still evolving, these perceptions persist.

Emily: Is the perception of threat real? What can companies do to change this perception?

Mike Vaishnav: The threat isn’t entirely real. While some routine jobs may be impacted, AI will create opportunities for more analytical roles. Companies need to educate their employees about AI, showing that it can complement human intelligence rather than replace it. People doing routine jobs can be redeployed to learn new skills.

Emily: We just spoke about job security. How real is this threat, or do you see it as an opportunity?

Mike Vaishnav: I see it more as an opportunity. While some entry-level positions may be affected, AI will create chances for employees to learn new skills and take on more analytical roles. The perceived threat can be mitigated through proper education and redeployment of resources.

Emily: Another threat you mentioned is data security. How real is it, and what can be done to mitigate it?

Mike Vaishnav: Data security is a real concern, but it has become more manageable with sophisticated AI systems. Ensuring data privacy and security involves everyone interacting with the data, not just the data administrators. Companies need to maintain high ethics, integrity, and trust in data handling to mitigate this threat.

Emily: That’s quite concerning for companies considering AI-driven processes. Thank you for your inputs, Mike. In the next part, we will cover the benefits of AI.

Emily: Welcome back, Mike. In the previous sections, we discussed the evolution of technology in finance and the threats posed by AI. Now, let’s explore the benefits of AI. Can you share some examples where AI simplifies the life of finance professionals?

Mike Vaishnav: AI can collect data, assist in decision-making, eliminate human error, simplify complex information, and reduce costs. It provides real-time data for analysis, making the finance function more efficient. AI helps finance professionals by automating data collection and analysis, saving time, and improving accuracy.

Emily: What skills should finance professionals acquire to take advantage of AI technology?

Mike Vaishnav: Finance professionals don’t need specific new skills because they are generally system-savvy. The key is to be open-minded and understand how to interpret and use AI-generated data. Trust in AI is built on understanding how data is collected and algorithms are written.

Emily: Can AI be a trusted friend, or should you always keep a watch on it? Can you give an example where AI can be fully trusted and another where its output must be reviewed?

Mike Vaishnav: AI can be a trusted friend for finance professionals if the data collection and algorithms are accurate. For instance, AI can reliably process and analyze large datasets. However, for complex decision-making, it’s essential to review AI outputs to ensure accuracy and relevance. Trust in AI comes with proper data handling and algorithm design, but human oversight remains crucial.

Emily: Thank you so much, Mike, for the insightful discussion. I’m sure this will provide our audience with clarity on embracing AI in their finance processes while avoiding potential threats.

Mike Vaishnav: Absolutely, thank you so much. It was a great discussion.