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Finance leaders - are we finally ready to eliminate the close?

Aaron Harris Profile picture for user Aaron Harris June 11, 2024
Technology may have come a long way, but closing the monthly books is a centuries-old issue and still fraught with frustration. What feels like eons later, Aaron Harris of Sage Intacct says there's light at the end of the tunnel.

Finance leader closing gap © suphakit73 -
(© suphakit73 -

The grumble of discontent stretches all the way back to the origin story of double-entry accounting. Finance professionals of every stripe and rank have hated the close since Luca Pacioli rolled out his treatise on double-entry accounting more than 500 years ago. Despite the many advances in process and productivity in the hundreds of years since our profession first started monthly closes, it can seem like things haven’t effectively improved all that much.

  • We’re still moving data from system to system – or sometimes even entering data manually. 
  • We’re still reconciling discrepancies between systems. 
  • We’re still chasing down employees to get things submitted, reviewed, and approved.

On the other hand, the close is a low-value, high-stakes proposition. When you close the books accurately and quickly, most outsiders merely shrug, happily content to not know the extraordinary lengths to which your team went. But get it wrong or take too long? Suddenly you and your team are on the hot seat.

Our customer surveys tell us businesses are spending about 25% of the finance department’s time on this sometimes-thankless endeavor. But what if we could get that time back and devote it to higher-value activities that truly move the business forward – stronger forecasting, M&A analyses, or business expansions? That’s the elusive promise of continuous-close (or better yet, no-close) accounting that increasingly taps into the power of artificial intelligence (AI) and automation to improve and accelerate the closing process. And it’s not just a pipedream. Companies from aircraft operators, dental practices, and utilities firms to chiropractors and even the YMCA are achieving tremendous gains minimizing their closing cycles.

It happens with AI

How are companies leveraging AI to close the gaps on closing the books? First, they’re focusing on data capture – a series of continuous activities that tap into an expanding assortment of finance-optimized machine learning models to code transactions for statutory reporting, financial reporting, and management reporting. These AI models – built by finance-centric firms, not the tech giants – learn the business’s chart of accounts, dimensions, and workflows. They’re good, and they’re getting better – already, these models can achieve 100% accuracy on nearly 70% of invoices.

Next, AI models can also aggregate and learn from the collective activity of thousands of companies. For example, once an AI model spots at least three organizations using the same vendor, it could create a type of digital fingerprint. At Sage, we’re employing patent-pending technology in this way to identify tens of thousands of vendors and millions of relationships, leading to 98% accuracy when we see those vendors in future workflows. Digital fingerprinting can even streamline vendor onboarding (a perennial finance bottleneck) or pinpoint potentially fraudulent transactions.

Finally, a continuous close requires continuous assurance. Naturally, you need to trust all that real-time data that you’re capturing and coding and transferring. That means finding exceptions and anomalies that might indicate clerical errors, billing mistakes, fraud, or meaningful changes in business performance that merit further investigation and human review. Reconciliation technology connects your business’s ledger across its ecosystem to banks and vendors, continuously reconciling the ledger. By turning 'doers' into 'reviewers', AI helps people focus less on tasks and more on decisions and outcomes.

Here's what I mean. Consider just one of the typical monthly close tasks: accruals. You have a purchase order and must reach out to a marketing manager to learn if your company can expect to be invoiced for the balance of the PO this month. With AI, your accounting system can automatically contact that manager, present recent invoices from that vendor, and, based on the history, recommend an amount to accrue. It’s faster and easier for both the finance team and the marketing manager. That’s just one small-but-impactful example of how the closing gets faster and easier.

Or consider the thankless task of chasing down employees for time sheets. With AI integration between accounting and HR systems, you can nudge employees to approve timesheets, confirm PTO, get the necessary approvals and signoffs, and (most importantly) avoid underbilling or overbilling mistakes. Your system could even offer to write emails that reach out to slow-paying customers. After a while, the system becomes very accurate in predicting when customers will pay and builds that information into a cash flow forecast.

Collectively, these improvements eliminate bottlenecks, improve accuracy, and move us toward that long-desired goal of eliminating the closing cycle entirely. We’re not there yet, but we can already see the transformational impact. For instance, CB Insights, a provider of market intelligence on private companies and investor activities, went from a 60-day soft close to a three-day hard close. Sure, that’s a meaningful productivity gain and cost reduction. But what elevates this improvement to a new level is what they did with those weeks of found time. CB Insights went to work on cash flow and brought their DSO down to by 10 days and created a reliable cash forecast that extends out 13 weeks. That translates into confidence to make strategic decisions.

Admittedly, in some respects, the closing cycle may never go away entirely – statutory reports and filings will likely remain a requirement for regulators and investors alike. But the steps that companies take in their pursuit of continuous closing will yield fruitful improvements that are well within reach today.

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