It’s time to close the books on manual account reconciliation processes. FinLync bank reconciliation software automatically helps you reconcile transactions and reconcile payments with fewer errors, and in far less time.
Manual account reconciliation is slow and error-prone. Accounting teams spend too much time logging in to multiple portals to reconcile bank statements with general ledgers because invoice references and remittance data is hidden in fields inside the ERP. The process is slow, tedious and open to errors, especially when discrepancies must be investigated to close the books each month.
FinLync account reconciliation software automates action. Embedded directly in your ERP, FinLync’s account reconciliation software reconciles your receivables, payables and general ledger with real-time bank account data. Using AI fed with the ocean of invoice data in the ERP, payment remittance data and acquirer statements, FinLync accelerates the process while directly offsetting any payment differences and clearing open items in real-time for complete reconciliation accounting. All of this allows for credit limits to be freed up as payments are received, deliveries released and more sales orders to be taken.
See current balances in real time; make sound decisions on accurate information, not estimates
No time spent on transaction-matching; more time available to investigate discrepancies or improve reconciliation processes
No separate account reconciliation or treasury management system to maintain; leverage existing ERP with embedded account reconciliation tools
Log into each bank portal
Transactions are received as they happen
Download previous-day bank statements
AI automatically matches and clears open items and posts to the general ledger
Load previous-day bank statements into ERP for each bank
Receive same-day exception alerts
Review postings and identify exceptions
As exceptions are resolved, the AI learns to automatically post similar items going forward
Canvass the wider team to resolve each exception
Exceptions are automatically bucketed by reason, allowing for prioritized process improvements
Wait for responses
AI/machine learning continues to improve hit rate; exceptions become rarer, dramatically improving reconciliation efficiency
Manually resolve exceptions by entering and attaching pertinent matching information to the record to reconcile
Repeat for each exception
Manually identify new logic to improve reconciliation efficiency
Define rigid new rules to apply going forward to improve efficiency
First, it’s about time: Transaction matching, line by line, is a thankless time-consuming task to complete. Worse, when exceptions are found, the analyst (or other designated treasury authority) has to search and hunt down people, reports, payments and/or invoices—often desk by desk, spreadsheet by spreadsheet —to determine the cause of the discrepancy. Given the variability of scenarios and reporting —understanding which financial data is needed and with what kind of handling—it’s nearly impossible for even the most determined of analysts to find, remember, and apply, the appropriate exception rules and deliver what is needed to the business for automated account reconciliation.
A company’s financial close is typically a very repetitive and overwhelming task with accounting teams spending hours each day reconciling the general ledger balances with information from the bank statement. Typically, there is a rush to close the books at the end of the month to begin all over again and get going on the next month. If the company is managing this process in spreadsheets, they are pulling data from their accounting system or ERP and pulling statements from bank portals manually. The analyst will take the list of transactions and begin to comb through the data one by one. This means that the process is prone to errors, like mistyping and accidentally adding extra digits, which are not easy to track.
For companies with more automated solutions where general ledger entries are exported from the Enterprise Resource Planning (ERP) system, and imported a separate solution, this means that there are extra steps involved before the team can begin the reconciliation process.
An automatic solution using advanced machine learning will help them make sure they have a documented review and approval process and help them address any discrepancies in real-time instead of waiting until it becomes a major problem.
Without bank reconciliation software, account reconciliation software, or accounting reconciliation software, analysts and/or accounting personnel must perform their work manually using archived emails, saved documents and binders of support references. Accuracy and consistency are compromised by variations in individual approaches and lack of an automated way to manage and apply exception rules.
With embedded SAP applications one does not need to export any data out of the ERP and into a spreadsheet or any other third-party system in order to run the accounting reconciliation process. They will be working off the most up to date general ledger information. Since FinLync connects to the banks in real-time via APIs, that means that companies are able to do a reconciliation process throughout the day.
They will be able to catch discrepancies as they happen. Since all the data is the most accurate, latest information, they will have ability to drill-down to the details. This is something they can’t do if exporting to a third-party vendor like another accounting reconciliation system or a TMS. This gives them full transparency/visibility to be able to address any data discrepancies more easily instead of going on a hunt to track where the error occurred.
For payment reconciliation and accounts receivable reconciliation among multiple banks and accounts, the software must be embedded in your ERP for maximum accuracy and efficiency, and minimal risk. Embedding the app minimizes the number of connections necessary for importing and exporting data, and allows teams to work simultaneously with the most up-to-date information across the enterprise, reducing delays. Direct ERP connectivity brings real-time data into play, allowing treasuries to work off the most up-to-date information available, whenever they need access to it.
Look beyond the basics. Automation, including machine learning or a rules-based approach, allows your system to learn from past events. Over time, the quality of predictive analytics improves, allowing the system to make more transaction matches over time, and to make informed recommendations when an exact match is not found. The iterative process improves the accuracy of the predictions by the solution, so that there are less discrepancies over time that treasury and accounting need to manage. With a user-friendly, and intuitive design, the accounting reconciliation process in FinLync creates automation and flexibility allowing users to focus on higher value functions instead of basic matching of transactions. The labor-intensive function of manually matching transactions will no longer be a drain on human capital. Reconciliation will go from a big month-end event to a more manageable and palatable ongoing process.
