Accounting Tech Market Alert: FloQast Provides AI-Powered Transaction Matching to Accelerate the Financial Close
Practice: Accounting Tech
Key Stakeholders: Chief Financial Officers, Chief Accounting Officers, Vice President of Finance, Vice President of Accounting, Corporate Controllers, Financial Close Directors and Managers, Accounting Directors and Managers, Finance Directors and Managers, Accounts Payable Directors and Managers, Financial Analysts, Staff Accountants
Why This Matters: Transaction matching, a key function in the account reconciliation process, is one of the most time-consuming challenges for timely financial close. Amalgam Insights estimates that 80% of mid-market companies currently conduct transaction matching either manually or with only the assistance of ungoverned spreadsheets. Current transaction matching solutions are either limited in transaction scope or extremely challenging for mid-market organizations to implement in a cost-effective and time-efficient manner.
Key Takeaway: With FloQast Matching, mid-sized enterprises and organizations have access to a scalable and usable transaction matching solution that will significantly reduce time-to-close by eliminating painful manual reviews for the vast majority of transactions and reducing matching error rates.
Contextualizing the Need for FloQast Matching
On April 2nd, 2019 at SuiteWorld, FloQast announced the launch of FloQast Matching, an AI-powered matching solution for accounting reconciliations that does not require prior rules definitions. This solution represents a new product line for FloQast that accompanies FloQast’s existing Close Management subscription.
As the most tedious part of account reconciliation, transaction matching is one of the most time-consuming and challenging aspects of the financial close, as this task traditionally requires a manual or machine-assisted process to review and categorize each transaction based on current business logic. However, reconciliation modules designed to support transaction matching are typically either focused on standard banking and merchant services transactions or are difficult to set up.
To understand why, consider that the concept of software-defined accounting transaction matching is not new to the accounting technology world, as Trintech has provided matching capabilities for over a decade and BlackLine launched finance controls that included matching capabilities in 2014. This history of automating transaction matching currently has inherent technical debt built in as these transaction matching capabilities are dependent on manual definitions of transactions that must be created and inputted by accountants, require significant work to effectively implement, and need to be updated based on general ledger changes driven by business unit additions and subtractions, mergers and acquisitions, or other structural business changes.
Because of the challenge of setting up these rules-based matching solutions serves as a barrier to entry, the vast majority of mid-market enterprise organizations choose to support transaction matching through manual tick-and-tie analysis and review with some spreadsheet assistance. This manual approach has been seen as the most cost and labor-effective method of closing the books as transaction matching automation has been seen as a cumbersome technology to implement.
Why is FloQast Transaction Matching Different From Previous Automated Reconciliation Methods?
In contrast to this legacy approach, FloQast uses artificial intelligence capabilities in the form of algorithms that automate the definition and creation of rules to define transactions on a one-to-one, one-to-many, or many-to-many basis that can be matched even when the ontology of the company’s general ledger does not match the categories and hierarchies of the financial systems being accessed. This cloud-based solution also scales to millions of records and auto-generates spreadsheet-formatted reconciliations for subsequent audit review. FloQast Matching effectively conducts days of manual transaction matching in a matter of minutes and does not require accounting teams to define and set up matching rules for their GL accounts. This results in both ease of implementation and ease of use in using FloQast Matching.
Amalgam Insights estimates based on an aggregation of multiple surveys and interviews that approximately 80% of companies between 250 and 5,000 employees conduct transaction matching solely through manual checks and spreadsheets and the rest use ERP financial modules to support financial transaction matching. Based on this perspective, Amalgam Insights believes that FloQast Matching provides a superior approach for mid-market organizations to accelerate the financial close and may be appropriate for larger organizations as well.
FloQast Matching is licensed separately from the current Enterprise Plan for close automation and the company pricing starts at $6,000 to support up to 10,000 transactions per month, which would be 60 cents per transaction. This pricing will be discounted based on volume. This solution is focused on organizations that have thousands of transactions that can take days to match accurately.
This pricing entry point greatly reduces the barrier to entry for automated transaction matching while removing the need to build transaction rules that are static, potentially arbitrary, and time-consuming to create and support.
The value of FloQast Matching from a financial Total Cost of Ownership should be considered based on two key metrics. The first is the pure cost of matching transactions, which Amalgam Insights estimates to be approximately $50 per hour for the fully-loaded cost of a staff accountant. The second metric is the potential value of work and skills reallocated to higher-value audit, process optimization, analytics, sales and supply chain management, and business due diligence activities. In addition, companies should consider the more intangible value of higher quality matching and reconciliation processes, which improve compliance, risk, and audit profiles.
Next Steps for FloQast Matching
Amalgam Insights notes that FloQast Matching is a novel solution for mid-market accounting organizations with a roadmap for improvement. Based on documents shared with Amalgam Insights, we believe FloQast Matching will be adding daily matching and roll over for unmatched transactions in the near future. In addition, future advancements for FloQast Matching are expected to include cross-currency and three-way matching as well as auto-creation of journal entries for unmatched transactions.
Amalgam Insights believes this roadmap is important in showing FloQast’s commitment to improving a product that can already be considered Best-in-Class in the specific task of transaction matching over time to enhance support for accountants.
The key recommendation Amalgam Insights provides is simply that accounting executives and managers need to be aware that automated transaction matching is now possible without the lengthy setup and ongoing commitment associated with rules-based process definitions. The promise of artificial intelligence in automating the most painstaking aspects of accounting is starting to take place and organizations should take advantage of this opportunity to reduce errors and increase accountant and financial analyst capacities for higher value work.
Amalgam Insights recommends FloQast Matching for companies with over 300 transactions a day. At this transactional volume, the time, cost and quality of FloQast Matching crosses over the manual capabilities of accountants, especially when considering the cost of reconciliation discrepancies.
Keep track of FloQast Matching’s capabilities over time, as this product will not be static. Amalgam Insights’ guidance suggests that this product will continue to improve over the next 18 months to provide an AI-powered transaction matching and consolidation solution that is on par with its rule-based peers, but without the ongoing need to also manage and administrate a set of rules that will create its own set of technical and compliance debt over time.