Managing risk is a core function of Finance and Accounting (F&A) teams. Nobody wants surprises, be it extra work, a poor audit, or a restatement. New technology like Artificial Intelligence, Machine Learning and Cloud Software - when used properly - can reduce many types of risks inherent in account reconciliation software selection and use.
Manually reconciling accounts or relying on an increasingly complex set of macros in Excel consumes precious time better spent on something else. But investing in new technologies or process improvements present their own risks: Will it work? What will it really cost us? Is it going to break something? This article outlines five types of risk you can reduce through by moving to a platform built on Machine Learning (a form of Artificial Intelligence) and sophisticated software, while also helping your team move beyond manual reconciliations and achieving a more efficient close process.
In a manual or Excel-driven reconciliation world, COSO 13 controls are fragile at best and non-existent at worst. This Internal Control risk leaves F&A teams fully exposed across a wide swath of F&A fiduciary responsibilities - from accuracy of financial statements, to protection of assets, to fraud prevention..
A machine learning + centralized software approach to account reconciliation reduces Internal Control risk by:
The human mind is an amazing computational organ. However, we are limited by how much data we can process visually – scanning sorted rows of transactions for example – and are prone to mis-read errors and the bias to be finished with our work. Whether keying in data incorrectly, inadvertently changing data format or row header in Excel, or failing to set-up a process when you go on vacation, humans are wonderful but not perfect.
A machine learning + software approach changes the responsibility of staff from one of a transaction matcher, to more of a reviewer and researcher. Modern machine learning based systems often record matching performance of 99%+ which means you can focus more on issues that need to be resolved vs manually managing the transaction matching process.
F&A teams often resort to batch-level reconciliations when they should truly match transactions, simply because of the sheer time needed to manually match. Yet, how many times have you run a batch level reconciliation only to find that the adjusted-totals don’t agree? The attempt to avoid time spent manually matching is quickly squandered as you go “treasure hunting” to find reconciling items that throw off your batch totals, and you end of spending far more time than if you had bitten the bullet and manually matched from the outset.
A modern machine learning + software approach can easily move you from a batch process to a transaction-level matching process because it can efficiently handle thousands or millions of transactions with a high degree of match accuracy. A system like Sigma IQ also identifies individual transactions that need your attention vs you having to spend hours (or days) finding them.
No one likes an qualified audit opinion, a material weakness finding, or having to do a restatement because of either poor process or internal controls (see risk one). It’s also not good for your career. A machine learning + software approach to account reconciliation can minimize audit and restatement risk in two ways:
There is always risk in bringing on a new technology or new process. Will it work as expected? Will it cost more to implement – in time or dollars – than promised? Will it really reduce my workload and provide an ROI?
Traditional account reconciliation software solutions are based on creating a series of specific rules to ingest and match transactions. This takes time, budget, and specific skills to implement – often by a system integrator team. You won’t know until after the system is implemented whether or how beneficial it is compared to your current process.
A machine learning approach to reconciliation can reduce purchase risk in four specific ways:
Finance and Accounting teams are under intense pressure to close more quickly and reconcile more accounts and more data faster and with greater accuracy. Or maybe you are simply interested in learning more about the next generation finance technology stack. In either case, you owe it to yourself to explore an AI-Machine Learning approach to account reconciliation. Not only will you reduce risk in the financial close process, but you will gain hours of your life back so that you can focus on higher value work or just go home at a decent hour and not work weekends.
Click here to watch a product demo video on Sigma IQ and/or schedule a demo to see how your account reconciliation process can be automated.
According to the APQC General Accounting Open Standards Benchmarking survey (2,300 companies participated) - Cycle Time for Monthly close ranges from 4.8 days or less for the top 25% of companies to 10 days or more for the bottom 25% of performers.Learn How To Be a Top Performer
According to a study by Robert Half & the Financial Executives Research Foundation (FERF), only 13% of F&A teams have utilized advancements in technology solutions, with the majority of CFO’s admitting they still struggle with painful aspects of account reconciliation.Read About AI-Driven Cost Savings
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Click here to watch a short product video or set up time to see for yourself how simple account reconciliation can be.