Automates daily cash reconciliation by matching invoices to bank transactions and generating a clear, actionable reconciliation table.
Imports invoices and daily bank statements, normalizes data, and aligns formats. Uses AI to match transactions to invoices with confidence scores and flags discrepancies. Produces a reconciliation table with matched, unmatched, and summary metrics.
Executes end-to-end cash reconciliation with clear outputs.
Import invoices and daily bank statements from OneDrive, Google Drive, or local storage.
OCR-extracts data from bank statements when needed and normalizes formats.
Compare statements to invoices with a confidence score for each match.
Flag transactions that are unapplied or require manual review.
Generate a reconciliation table with match percentages, counts, and summaries.
Export results and log discrepancies for audit and future improvements.
This AI agent replaces fragmented manual work with a predictable execution flow.
Three-step flow you can run daily without technical effort.
Load invoices and bank statements from preferred storage, apply OCR to PDFs, and standardize formats for comparison.
Run the AI agent to compare transactions, assign confidence scores, and mark items needing manual review.
Produce a reconciliation table with metrics and export results for audits; reviewer handles only exceptions.
A realistic daily scenario showing time-to-value.
A mid-sized retailer processes daily bank statements, about 120 transactions. The AI Agent runs after business hours, matching invoices to statements, flagging 2 open items for review, and delivering a reconciliation table within 15 minutes. The operator reviews the 2 exceptions, approves the matches, and the daily cash position is updated in the ERP by morning.
Key roles that gain immediate workflow advantages.
Relieves manual matching workload and reduces data-entry errors.
Gains daily visibility into cash flow and reconciliation status.
Speeds credit and payment matching across invoices and bank data.
Delivers auditable, consistent reconciliation records for audits.
Access to transparent match reasons and receipts for audits.
Improves process reliability and financial controls across teams.
Connects to storage, OCR, and AI services to run autonomous reconciliations.
Drives AI-based matching and confidence scoring inside the AI agent.
Extracts text from PDF bank statements for accurate data extraction.
Fetches bank statements and invoice files stored in OneDrive.
Alternative storage for invoices or bank statements and retrieval.
Reads and writes to Excel/CSV invoices and reconciliation outputs.
Provides a local storage option for invoices and statements.
Practical scenarios where daily automation delivers measurable results.
Common questions about setup, operation, and security.
This AI agent automates the end-to-end process of reconciling bank statements to invoices. It imports data, applies OCR where needed, runs AI-based matching with confidence scoring, flags exceptions, and delivers an auditable reconciliation table. Users interact mainly with the output and flagged items, while the AI agent handles the heavy lifting. It integrates with storage options and spreadsheet tools to fit existing workflows.
The AI model evaluates how closely a bank transaction aligns with an invoice by amount, date tolerance, and reference matching. It returns a score that reflects the likelihood of a correct match, allowing users to filter or review high-uncertainty items. Scores are logged with each reconciliation run for auditability. Thresholds can be adjusted to suit business rules.
Invoices should be stored in Excel or CSV format, while bank statements can come from PDFs (OCR-enabled) or already structured CSV/Excel. The AI agent normalizes data into a common schema for reliable matching. If headers differ, mappings can be updated in configuration. The system can handle multiple currencies if configured.
Thresholds for date tolerances, amount variances, and confidence scoring are set in the configuration. They can be adjusted through code nodes or an admin interface, depending on deployment. Changes apply to future reconciliations and can be tested with a manual run before scheduling. This ensures alignment with business rules.
Data security depends on the storage and AI service configurations. API keys are kept in secure sources, communications use encryption, and access is restricted by permissions. Reconciliation results can be stored in secure locations and access controlled. For sensitive environments, run the agent in a private network with restricted outbound access.
Yes. The agent can start with a manual test and then switch to a daily or business-day schedule. Schedules trigger the AI matching, output generation, and notification steps automatically. You can also pause or adjust run times to fit business hours and avoid peak processing periods.
Multi-currency support requires consistent currency data in both invoices and bank statements. The agent can normalize currency values and apply exchange-rate logic if configured. For accurate totals, ensure currency fields are present and correctly mapped in the source files. The reconciliation table will reflect converted values when enabled.
Automates daily cash reconciliation by matching invoices to bank transactions and generating a clear, actionable reconciliation table.
