F&A processes have come a long way since Excel sheets, macros and formulas were first used for reconciliation. Learn why AI - specifically Machine Learning - works more efficiently and effectively for modern transaction matching.
As digital transformation sweeps across the enterprise landscape, F&A processes continue to evolve. The decades-old manual process of entering data into a spreadsheet for reconciliation purposes has given way to digital reconciliation, with the advent of automation technology to make it faster and more efficient.
However, even automated processes have evolved in the last few years with the advances made in machine learning and AI.
What do these advances mean for F&A teams today?To illustrate the profound implications of AI and machine learning for F&A, consider the evolution of the transaction matching process in reconciliation.
From the earliest days, F&A departments have largely relied on manual processes to reconcile accounts. Moving over the years from paper systems to the ubiquitous spreadsheet model, F&A processes have been time-intensive and somewhat error-prone. An oft-referenced study found that 90 percent of spreadsheets contain at least one human error. Innocuous as they may seem, those errors are more than just typos. If the problem isn't noticed during the account reconciliation process, it could send turbulence into the company's balance sheet.
Added to that is the sheer inefficiency of manually entering data into a spreadsheet for transaction matching. At the enterprise level, F&A teams are tasked with reconciling hundreds of thousands of individual line items against multiple data sources to substantiate their balance sheets. Early rules-based automated transaction matching alleviated some of these challenges, but at a time when enterprise-level F&A teams are feeling the crunch of too little time with too many tasks to perform, it is clear that a better alternative is needed.
Legacy rule-based automation relied entirely on human analysis and the experience of the rule-maker. The drawback to this model is that no matter how talented the human, a certain amount of bias exists. Further, rules-based automation designed on human analytic ability is hard to scale.
Adding or deleting rules to rectify one error can easily cause unanticipated ripple effects that skew transaction matching and lead to inaccuracies and the need for additional manual reconciliation – the opposite of what F&A teams want to accomplish by automating transaction matching in the first place.
To combat this, AI and machine learning are being put to work in automated transaction matching and reconciliation to prevent costly human error and streamline transaction matching at the same time. Rules-based systems built by humans can only learn at the speed of human learning.
However, because AI can sift through mountains of data in a fraction of the time compared to humans, it’s no surprise that the technology is now being tasked with finding every duplication, discrepancy, and deficiency on record.
Machine learning can perform complex actions in a fraction of the time.
Legacy rule-based automation is fairly rigid, but automation based on machine learning is agile by comparison. As more powerful computational programming languages and more sophisticated algorithms evolve, machine learning enables automation at a scope and scale previously unheard of.
Instead of relying on rules-based logic such as conditional rules (if X, then Y), Boolean rules, or fixed mathematical formulas, machine learning technology uses more advanced algorithms which are free of any human bias, much more agile than simple rules-based approaches, and able to continue learning and adapting as more data is consumed over time.
At its very core, machine learning thrives on redundancy; it “learns” an action or task and can repeat that same action or task under the right parameters. Modern AI can learn from accountants’ manual activities, such as applying payments to active invoices or matching transactions.
Automation tools tap into your important data sources such as ERPs, point of sale systems, payroll, and bank detail to understand patterns. Using these data points, systems can identify matches and exceptions, and even make suggestions to resolve potential issues.
Within moments, what once took an F&A team hours of tedious work to match hundreds of thousands of transactions to line items on the balance sheet can be accomplished easily by harnessing the power and agility of AI and machine learning.
Automated transaction matching via machine learning technology enables your F&A team to streamline reconciliation processes and improve accuracy at the same time. Sigma IQ has combined advanced AI-enhanced automation with industry-leading reconciliation tools, saving F&A departments hours of tedious work while ensuring accurate data quality. See how it works using your own data by scheduling a free demonstration today.
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