AI Financial Reconciliation Agent
Close your books in hours, not days
Close your books in hours, not days
Delegate the tedious work of reconciliation to a specialized AI agent. It automates financial reconciliation by matching transactions across multiple systems and documents, allowing your team to focus on resolving exceptions, not finding them.
Why V7 Go
Why V7 Go
Multi-Way Transaction Matching
Perform two-way, three-way, or multi-way matching across documents. The agent can reconcile invoices to payments, bank statements to the general ledger, or all three simultaneously to ensure every transaction is accounted for.
Multi-Way Transaction Matching
Perform two-way, three-way, or multi-way matching across documents. The agent can reconcile invoices to payments, bank statements to the general ledger, or all three simultaneously to ensure every transaction is accounted for.
Multi-Way Transaction Matching
Perform two-way, three-way, or multi-way matching across documents. The agent can reconcile invoices to payments, bank statements to the general ledger, or all three simultaneously to ensure every transaction is accounted for.
Automated Data Ingestion
Connect directly to ERPs and accounting systems or upload documents in any format. The agent extracts and normalizes transaction data from PDF bank statements, spreadsheet exports, and system-generated reports.
Automated Data Ingestion
Connect directly to ERPs and accounting systems or upload documents in any format. The agent extracts and normalizes transaction data from PDF bank statements, spreadsheet exports, and system-generated reports.
Automated Data Ingestion
Connect directly to ERPs and accounting systems or upload documents in any format. The agent extracts and normalizes transaction data from PDF bank statements, spreadsheet exports, and system-generated reports.
Intelligent Matching Logic
The agent uses sophisticated matching algorithms guided by configurable business rules. It can match transactions based on multiple keys (amount, date, invoice number) and handle variations in data formatting or minor discrepancies within set tolerances.
Intelligent Matching Logic
The agent uses sophisticated matching algorithms guided by configurable business rules. It can match transactions based on multiple keys (amount, date, invoice number) and handle variations in data formatting or minor discrepancies within set tolerances.
Intelligent Matching Logic
The agent uses sophisticated matching algorithms guided by configurable business rules. It can match transactions based on multiple keys (amount, date, invoice number) and handle variations in data formatting or minor discrepancies within set tolerances.
Automated Exception Handling
Instead of just failing on a mismatch, the agent flags any transactions that cannot be reconciled automatically. It provides a clear report of all exceptions, categorizes the likely reason for the mismatch, and routes it for human review.
Automated Exception Handling
Instead of just failing on a mismatch, the agent flags any transactions that cannot be reconciled automatically. It provides a clear report of all exceptions, categorizes the likely reason for the mismatch, and routes it for human review.
Automated Exception Handling
Instead of just failing on a mismatch, the agent flags any transactions that cannot be reconciled automatically. It provides a clear report of all exceptions, categorizes the likely reason for the mismatch, and routes it for human review.
Sub-Ledger Reconciliation
Automate the reconciliation of sub-ledgers (like Accounts Payable and Accounts Receivable) to the General Ledger. This ensures that detailed transactional records are perfectly aligned with summary-level financial reporting.
Sub-Ledger Reconciliation
Automate the reconciliation of sub-ledgers (like Accounts Payable and Accounts Receivable) to the General Ledger. This ensures that detailed transactional records are perfectly aligned with summary-level financial reporting.
Sub-Ledger Reconciliation
Automate the reconciliation of sub-ledgers (like Accounts Payable and Accounts Receivable) to the General Ledger. This ensures that detailed transactional records are perfectly aligned with summary-level financial reporting.
Audit-Ready Trail and Reporting
Generate a complete, verifiable log of the entire reconciliation process. The report details which transactions were matched, the rules applied, and provides a clear list of all outstanding items, which helps with audits and internal controls.
Audit-Ready Trail and Reporting
Generate a complete, verifiable log of the entire reconciliation process. The report details which transactions were matched, the rules applied, and provides a clear list of all outstanding items, which helps with audits and internal controls.
Audit-Ready Trail and Reporting
Generate a complete, verifiable log of the entire reconciliation process. The report details which transactions were matched, the rules applied, and provides a clear list of all outstanding items, which helps with audits and internal controls.
Why V7 Go
Process bank statements, invoices, and general ledger exports.
Get lists of matched transactions and exception reports.
Workflow
Workflow
Import your files
SAP
,
NetSuite
,
QuickBooks
Bank Statements (PDF, CSV)
Bank Statements (PDF, CSV)
Bank Statements (PDF, CSV)
Bank Statements (PDF, CSV)
General Ledger (GL) Exports
General Ledger (GL) Exports
General Ledger (GL) Exports
General Ledger (GL) Exports
Sales & Purchase Invoices
Sales & Purchase Invoices
Sales & Purchase Invoices
Sales & Purchase Invoices
Credit Card Statements
Credit Card Statements
Credit Card Statements
Credit Card Statements
Payment Gateway Reports (e.g., Stripe)
Payment Gateway Reports (e.g., Stripe)
Payment Gateway Reports (e.g., Stripe)
Payment Gateway Reports (e.g., Stripe)
Accounts Payable/Receivable Ledgers
Accounts Payable/Receivable Ledgers
Accounts Payable/Receivable Ledgers
Accounts Payable/Receivable Ledgers
List of Matched Transactions
List of Matched Transactions
List of Matched Transactions
List of Matched Transactions
Unmatched Items & Exception Report
Unmatched Items & Exception Report
Unmatched Items & Exception Report
Unmatched Items & Exception Report
Reconciliation Summary Report
Reconciliation Summary Report
Reconciliation Summary Report
Reconciliation Summary Report
Suggested Journal Entries for Discrepancies
Suggested Journal Entries for Discrepancies
Suggested Journal Entries for Discrepancies
Suggested Journal Entries for Discrepancies
Reason Codes for Mismatches
Reason Codes for Mismatches
Reason Codes for Mismatches
Reason Codes for Mismatches
Complete Audit Trail Log
Complete Audit Trail Log
Complete Audit Trail Log
Complete Audit Trail Log
Amount Variances
Amount Variances
Amount Variances
Amount Variances
Timing Differences
Timing Differences
Timing Differences
Timing Differences
Missing Transactions
Missing Transactions
Missing Transactions
Missing Transactions
Duplicate Entries
Duplicate Entries
Duplicate Entries
Duplicate Entries
Time comparison
Time comparison
Time comparison
Time comparison
Traditional way
3-5 days per reconciliation cycle
3-5 days per reconciliation cycle
With V7 Go agents
2-4 hours
2-4 hours
Average time saved
90%
90%
V7 Go
V7 Go
V7 Go
V7 Go
AI-powered reconciliation
for accounting teams
Accounting teams spend up to 40% of their month-end close process manually reconciling transactions. This involves painstakingly matching line items from bank statements, invoices, and the general ledger, often using complex spreadsheets. The process is slow, prone to human error, and a major bottleneck to producing timely financial reports. V7 Go's Financial Reconciliation Agent automates this workflow by ingesting data from any source and performing multi-way matching to identify and flag exceptions in minutes, not days.
Financial Controllers
Gain confidence in financial data and accelerate the month-end close. Free up your team from manual tasks to focus on variance analysis and strategic financial work.
Financial Controllers
Gain confidence in financial data and accelerate the month-end close. Free up your team from manual tasks to focus on variance analysis and strategic financial work.
Financial Controllers
Gain confidence in financial data and accelerate the month-end close. Free up your team from manual tasks to focus on variance analysis and strategic financial work.
Financial Controllers
Gain confidence in financial data and accelerate the month-end close. Free up your team from manual tasks to focus on variance analysis and strategic financial work.
Staff Accountants & AP/AR Teams
Eliminate the most tedious part of the job. Instead of manually 'ticking and tying' thousands of lines, focus on investigating and resolving the few complex discrepancies the AI flags.
Staff Accountants & AP/AR Teams
Eliminate the most tedious part of the job. Instead of manually 'ticking and tying' thousands of lines, focus on investigating and resolving the few complex discrepancies the AI flags.
Staff Accountants & AP/AR Teams
Eliminate the most tedious part of the job. Instead of manually 'ticking and tying' thousands of lines, focus on investigating and resolving the few complex discrepancies the AI flags.
Staff Accountants & AP/AR Teams
Eliminate the most tedious part of the job. Instead of manually 'ticking and tying' thousands of lines, focus on investigating and resolving the few complex discrepancies the AI flags.
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Next steps
Close your books faster and with greater confidence.
Let AI handle the tedious work of reconciliation.
You’ll hear back in less than 24 hours
Next steps
Close your books faster and with greater confidence.
Let AI handle the tedious work of reconciliation.
You’ll hear back in less than 24 hours
Next steps
Close your books faster and with greater confidence.
Let AI handle the tedious work of reconciliation.
You’ll hear back in less than 24 hours
Next steps
Close your books faster and with greater confidence.
Let AI handle the tedious work of reconciliation.
FAQ
FAQ
FAQ
FAQ
Have questions?
Find answers.
What is multi-way matching?
It's the process of verifying a transaction against multiple sources. For example, a three-way match confirms that the details on a purchase order, the vendor invoice, and the payment record in the bank statement are all consistent. The agent automates this complex matching process.
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What is multi-way matching?
It's the process of verifying a transaction against multiple sources. For example, a three-way match confirms that the details on a purchase order, the vendor invoice, and the payment record in the bank statement are all consistent. The agent automates this complex matching process.
+
What is multi-way matching?
It's the process of verifying a transaction against multiple sources. For example, a three-way match confirms that the details on a purchase order, the vendor invoice, and the payment record in the bank statement are all consistent. The agent automates this complex matching process.
+
What is multi-way matching?
It's the process of verifying a transaction against multiple sources. For example, a three-way match confirms that the details on a purchase order, the vendor invoice, and the payment record in the bank statement are all consistent. The agent automates this complex matching process.
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What financial systems can the agent connect to?
The agent can integrate with major ERP and accounting platforms like SAP, NetSuite, Oracle, and QuickBooks via API. It can also process exported files (like CSVs or PDFs) from any system, including bank portals and credit card providers.
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What financial systems can the agent connect to?
The agent can integrate with major ERP and accounting platforms like SAP, NetSuite, Oracle, and QuickBooks via API. It can also process exported files (like CSVs or PDFs) from any system, including bank portals and credit card providers.
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What financial systems can the agent connect to?
The agent can integrate with major ERP and accounting platforms like SAP, NetSuite, Oracle, and QuickBooks via API. It can also process exported files (like CSVs or PDFs) from any system, including bank portals and credit card providers.
+
What financial systems can the agent connect to?
The agent can integrate with major ERP and accounting platforms like SAP, NetSuite, Oracle, and QuickBooks via API. It can also process exported files (like CSVs or PDFs) from any system, including bank portals and credit card providers.
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How does the agent handle unstructured documents like PDF bank statements?
V7 Go uses advanced document processing AI to accurately extract transactional data, including dates, descriptions, and amounts, from any PDF or scanned document. It then normalizes this data into a structured format ready for reconciliation.
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How does the agent handle unstructured documents like PDF bank statements?
V7 Go uses advanced document processing AI to accurately extract transactional data, including dates, descriptions, and amounts, from any PDF or scanned document. It then normalizes this data into a structured format ready for reconciliation.
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How does the agent handle unstructured documents like PDF bank statements?
V7 Go uses advanced document processing AI to accurately extract transactional data, including dates, descriptions, and amounts, from any PDF or scanned document. It then normalizes this data into a structured format ready for reconciliation.
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How does the agent handle unstructured documents like PDF bank statements?
V7 Go uses advanced document processing AI to accurately extract transactional data, including dates, descriptions, and amounts, from any PDF or scanned document. It then normalizes this data into a structured format ready for reconciliation.
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How are the matching rules configured?
You can define your own reconciliation rules within a no-code interface. This includes setting tolerance levels for amounts (e.g., match if within $0.05), date ranges, and defining which fields to use as primary and secondary matching keys (e.g., invoice number, customer name).
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How are the matching rules configured?
You can define your own reconciliation rules within a no-code interface. This includes setting tolerance levels for amounts (e.g., match if within $0.05), date ranges, and defining which fields to use as primary and secondary matching keys (e.g., invoice number, customer name).
+
How are the matching rules configured?
You can define your own reconciliation rules within a no-code interface. This includes setting tolerance levels for amounts (e.g., match if within $0.05), date ranges, and defining which fields to use as primary and secondary matching keys (e.g., invoice number, customer name).
+
How are the matching rules configured?
You can define your own reconciliation rules within a no-code interface. This includes setting tolerance levels for amounts (e.g., match if within $0.05), date ranges, and defining which fields to use as primary and secondary matching keys (e.g., invoice number, customer name).
+
How does this differ from the reconciliation tools built into our ERP?
ERP reconciliation modules are often rigid and struggle to incorporate data from external or unstructured sources like bank statements from different banks. V7 Go's agent is flexible, connects across multiple systems and document types, and uses more sophisticated matching logic to handle real-world data complexity.
+
How does this differ from the reconciliation tools built into our ERP?
ERP reconciliation modules are often rigid and struggle to incorporate data from external or unstructured sources like bank statements from different banks. V7 Go's agent is flexible, connects across multiple systems and document types, and uses more sophisticated matching logic to handle real-world data complexity.
+
How does this differ from the reconciliation tools built into our ERP?
ERP reconciliation modules are often rigid and struggle to incorporate data from external or unstructured sources like bank statements from different banks. V7 Go's agent is flexible, connects across multiple systems and document types, and uses more sophisticated matching logic to handle real-world data complexity.
+
How does this differ from the reconciliation tools built into our ERP?
ERP reconciliation modules are often rigid and struggle to incorporate data from external or unstructured sources like bank statements from different banks. V7 Go's agent is flexible, connects across multiple systems and document types, and uses more sophisticated matching logic to handle real-world data complexity.
+
Can the agent handle high volumes of transactions?
Yes, the agent is built to scale. It can process hundreds of thousands of transactions in a fraction of the time it would take a human team, making it ideal for businesses of any size, especially those with high transaction volumes.
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Can the agent handle high volumes of transactions?
Yes, the agent is built to scale. It can process hundreds of thousands of transactions in a fraction of the time it would take a human team, making it ideal for businesses of any size, especially those with high transaction volumes.
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Can the agent handle high volumes of transactions?
Yes, the agent is built to scale. It can process hundreds of thousands of transactions in a fraction of the time it would take a human team, making it ideal for businesses of any size, especially those with high transaction volumes.
+
Can the agent handle high volumes of transactions?
Yes, the agent is built to scale. It can process hundreds of thousands of transactions in a fraction of the time it would take a human team, making it ideal for businesses of any size, especially those with high transaction volumes.
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