Finance team spending close week manually reconciling transactions that a bot could match?
Invoice processing backlog delaying supplier payments and straining vendor relationships?
RPA in Finance
Finance teams process high volumes of structured, rule-based transactions -- invoice matching, bank reconciliation, accounts payable runs, period-end close tasks, and regulatory reporting. Most of this work follows the same logic every time. A bot executes it faster, with fewer errors, and with a complete audit trail.
We build robotic process automation systems for finance operations -- accounts payable automation, financial reconciliation, period-end close support, and regulatory reporting -- so your finance team focuses on analysis, decisions, and the work that requires financial judgment.
Accounts payable automation from invoice receipt to payment approval
Bank and financial reconciliation without manual matching
Period-end close task automation that compresses your close cycle
Regulatory reporting assembled automatically from your source systems
RaftLabs builds robotic process automation systems for finance teams -- accounts payable processing, invoice matching, bank and ledger reconciliation, period-end close automation, and regulatory reporting assembly. Finance RPA handles the high-volume, rule-based transaction processing that currently consumes finance team capacity, replacing it with bots that run faster, produce consistent output, and maintain a complete audit trail. Most finance RPA projects deliver in 8--12 weeks at a fixed cost.
Finance automation reduces cost, errors, and close time simultaneously
Manual accounts payable processing costs more per invoice than automated processing. Manual reconciliation introduces errors that take time to find and fix. Manual period-end close takes longer and creates more last-minute pressure than an automated process would.
Finance RPA addresses all three at once -- lower processing cost, fewer errors, and a faster close cycle -- because the same root cause (manual, repetitive transaction processing) drives all three problems.
Finance processes we automate
Accounts payable automation
End-to-end AP automation from invoice receipt to payment. AI-powered invoice extraction from PDF, email, and EDI, matched against purchase orders and delivery confirmations. Approval routing based on your authorisation matrix. ERP posting for matched invoices. Exception queue for invoices that require human review. Payment batch preparation on your payment schedule. The AP process that currently takes a team days per month runs in hours, with lower error rates and a complete audit trail.
Bank and ledger reconciliation
Automated matching of bank statement transactions to ledger entries. Configurable matching rules for direct matches, partial matches, and multi-line consolidations. Unmatched items flagged with suggested resolutions for reviewer action. Daily or weekly reconciliation runs without manual effort. Period-end reconciliation packs generated automatically with matched items documented and exceptions explained. The reconciliation that used to take your team close week now runs overnight.
Period-end close automation
Automated execution of repeatable period-end close tasks -- accrual journal posting, depreciation runs, intercompany eliminations, and management pack data assembly. Close task checklists with automated status tracking and escalation for overdue items. Variance analysis data assembled automatically for the management review meeting. The close cycle that currently compresses your team compresses the time, not the team.
Regulatory reporting
Automated assembly of regulatory submissions from data across your finance, risk, and operational systems. Data extraction, transformation, and validation against submission templates. Submission tracking and deadline management. Regulatory change monitoring to update templates when requirements change. The reporting that keeps your organisation compliant runs automatically and delivers a submission-ready output to the reviewer -- not a stack of spreadsheets.
Financial data extraction and processing
Automated extraction of financial data from source systems -- bank statements, supplier invoices, expense reports, and transaction feeds -- and processing into your ERP or data warehouse. Document AI for unstructured financial documents. Transformation and normalisation for data from sources with different formats. The data pipeline that makes your finance data available for analysis without a team member spending time on extraction and formatting.
Management reporting
Automated assembly of management reports -- P&L, cash flow, balance sheet, KPI dashboards, and budget vs. actual analysis -- from your source systems. Scheduled delivery to report recipients in PDF, Excel, or directly to your BI tool. The management reporting cycle that currently requires a finance team member to spend days compiling data runs automatically, with the team's time focused on analysis and commentary rather than data assembly.
Tell us which finance process consumes the most team time.
Process, current systems, and transaction volume. We'll design the automation and give you a fixed cost.
Frequently asked questions
The highest-value finance automation targets are high volume, rule-based, and involve structured data from identifiable sources. Top processes: accounts payable (invoice extraction, PO matching, approval routing, payment processing), bank reconciliation (matching bank statement transactions to ledger entries, flagging exceptions), intercompany reconciliation, period-end journal entry posting, tax data compilation for filing, regulatory report assembly (CRR, DSCR, Pillar 3, SEC filings), and management report generation. Processes with high error costs -- regulatory submissions, tax filings, supplier payments -- have the clearest automation ROI.
We integrate with ERP and accounting platforms via API, direct database access, or UI automation depending on what your system exposes. Common integrations: SAP (FI/CO modules), Oracle Financials, Microsoft Dynamics 365 Finance, NetSuite, Sage, Xero, and QuickBooks. For invoice processing, we typically combine document AI (extracting structured data from PDF invoices) with ERP integration (posting the extracted data to the correct accounts). The integration approach is determined during scoping based on your specific ERP version and data model.
RPA handles the straight-through cases automatically -- the transactions that match, the invoices that have a PO, the reconciliation items that clear. Exceptions are flagged and routed to a human review queue with the context the reviewer needs to resolve them. A well-designed finance RPA system aims for 80--90% straight-through processing, with human effort focused on the 10--20% that requires judgment. The exception handling design is as important as the automation logic -- we build both as part of the same system.
Finance automation must produce audit-ready output. Every bot action is logged -- what data was accessed, what decision was made, what system was updated, and when. Reconciliation outputs include the matching logic and any manual overrides. Period-end close automation maintains a timestamped record of each close task. This audit trail is more complete and consistent than manual processes, which often rely on individual staff members remembering what they did and documenting it after the fact. We build audit logging as a core requirement, not an afterthought.
A focused finance automation system -- one process automated (e.g., AP invoice matching and approval routing), including bot development, testing in your environment, and deployment -- typically runs $20,000--$50,000. Multi-process programmes covering AP, reconciliation, and period-end close run $50,000--$130,000. Cost depends on the number of processes, ERP complexity, and the sophistication of the exception handling required. We scope every project before pricing it.