Overview
The Find Duplicate Invoices calculator identifies and summarizes invoices that may have been entered multiple times in your accounts payable process. This specialized tool detects various types of duplicate patterns, from exact matches to invoices with subtle differences in amounts, dates, or due dates. It provides actionable insights for duplicate payment prevention, accounts payable auditing, and data quality improvement.
IMPORTANT: This calculator requires the Find Duplicate Invoices enrichment operator to be applied first. The enrichment operator performs the actual duplicate detection, while the calculator presents the results in an organized, actionable format.
Common Uses
- Prevent duplicate payments before they occur by identifying invoices entered multiple times
- Conduct accounts payable audits by reviewing and resolving duplicate invoice issues
- Assess data quality in invoice processing systems and quantify duplicate entry problems
- Track financial exposure by calculating the total value of unresolved duplicate invoices
- Monitor resolution progress through workflow tracking of duplicate investigation and remediation
- Analyze duplicate patterns to identify root causes such as system errors or process gaps
Settings
Invoice Number: Select the case attribute containing the invoice number or document number. This field is used for display purposes and calculating statistics about duplicate groups.
Vendor: Select the case attribute containing the vendor or supplier name. This helps identify which vendors have duplicate invoice issues and provides context for duplicate groups.
Invoice Amount: Select the case attribute containing the invoice amount or total value. When specified, the calculator computes the total monetary value of all duplicate invoices, helping quantify the financial risk exposure.
Invoice Date: Select the case attribute containing the invoice document date. This field provides additional context for understanding when duplicates were created and helps distinguish between legitimate recurring invoices and true duplicates.
Due Date: Select the case attribute containing the invoice payment due date. When specified, the calculator identifies the closest upcoming payment deadline among all duplicate invoices, helping prioritize which duplicates to resolve first.
Max Rows: Specify the maximum number of duplicate groups to display in the output table.
Examples
Example 1: Identifying High-Value Duplicate Invoices
Scenario: Your accounts payable team needs to identify potential duplicate invoices before the next payment run. You want to understand both the number of duplicates and the total financial exposure to prioritize which duplicates to investigate first.
Settings:
- Invoice Number: InvoiceNumber
- Vendor: VendorName
- Invoice Amount: InvoiceAmount
- Invoice Date: InvoiceDate
- Due Date: PaymentDueDate
- Max Rows: 100
Output:
The calculator displays three key summary metrics at the top:
Total Duplicate Value: $284,750.00 - This represents the total monetary value of all invoices identified as potential duplicates across all groups.
Number of Duplicates: 47 invoices - This is the total count of individual invoice cases flagged as duplicates.
Closest Due Date: 2025-10-25 - This shows the earliest upcoming payment deadline among all duplicate invoices, helping you prioritize urgent reviews.
The main table shows one row per duplicate group with these columns:
- Group Name: Unique identifier for each set of duplicate invoices (e.g., "ACME_Corp_INV-12345")
- Match Type: The type of duplicate detected (Exact, Invoice Amount Change, Invoice Date Change, Invoice Due Date Change)
- Group Count: Number of invoices in this duplicate group (e.g., 2, 3, or more)
- Group Value: Total invoice amount for all invoices in this group
- Resolution Status: Indicates whether the duplicate has been reviewed and resolved
- Resolved By: Name of the person who investigated the duplicate
- Resolved Time: When the duplicate was marked as resolved
Insights:
The summary metrics immediately reveal significant financial exposure from duplicates. With nearly $285,000 in potential duplicate payments and a due date just days away, this requires urgent attention.
Looking at the Match Type column helps prioritize investigation:
- "Exact" matches (same vendor, invoice number, amount, and date) are most likely true duplicates requiring immediate action
- "Invoice Amount Change" matches may indicate legitimate invoice corrections or adjustments
- "Invoice Date Change" or "Invoice Due Date Change" matches might be data entry errors worth investigating
The Group Count shows how many times each invoice appears. A count of 2 suggests a simple duplicate entry, while higher counts (3, 4, or more) may indicate systemic issues like automated processes creating repeated entries.
By filtering the results to show only unresolved duplicates with due dates in the next week, you can create a prioritized action list for your team to investigate before payment processing.
Example 2: Tracking Duplicate Resolution Progress
Scenario: Your organization ran the duplicate detection last month and assigned team members to investigate each duplicate group. Now you want to monitor resolution progress and ensure all duplicates are addressed before month-end closing.
Settings:
- Invoice Number: InvoiceNumber
- Vendor: VendorName
- Invoice Amount: InvoiceAmount
- Invoice Date: InvoiceDate
- Due Date: PaymentDueDate
- Max Rows: 500
Output:
The main table includes resolution tracking columns that show the workflow status:
- Invoices with "Resolved By" and "Resolved Time" values show completed investigations
- Empty resolution fields indicate duplicates still pending review
- The "Not A Duplicate" flag shows cases marked as false positives (legitimate invoices incorrectly flagged)
You can calculate the resolution rate: If 35 out of 47 duplicates have been resolved, that's 74% completion with 12 duplicates still requiring attention.
Insights:
Resolution tracking transforms duplicate detection from a one-time analysis into an ongoing workflow. Team members can be assigned specific duplicate groups to investigate, and their progress is visible in the output.
The "Not A Duplicate" flag is particularly valuable for understanding false positive patterns. For example:
- Recurring invoices from the same vendor for the same amount (like monthly service contracts) may legitimately appear as duplicates
- Volume purchase agreements might result in multiple invoices with identical amounts
- Different invoice numbers that look similar but represent separate transactions
By reviewing cases marked "Not A Duplicate," you can refine the duplicate detection criteria to reduce false positives in future runs, making the analysis more accurate over time.
The Resolved Time column helps identify bottlenecks. If duplicates assigned two weeks ago remain unresolved, you may need to reallocate resources or escalate specific complex cases.
Example 3: Analyzing Duplicate Patterns for Root Cause Analysis
Scenario: After identifying numerous duplicates, you want to understand what's causing them. Are they data entry errors, system integration issues, or process problems? Analyzing the match types and patterns will help you implement preventive measures.
Settings:
- Invoice Number: InvoiceNumber
- Vendor: VendorName
- Invoice Amount: InvoiceAmount
- Invoice Date: InvoiceDate
- Due Date: PaymentDueDate
- Max Rows: 200
Output:
The Match Type column reveals distinct patterns:
- 65% "Exact" matches - Same vendor, invoice number, amount, and date
- 20% "Invoice Amount Change" - Same vendor and invoice number, different amounts
- 10% "Invoice Date Change" - Same vendor, invoice number, and amount, different dates
- 5% "Invoice Due Date Change" - All fields match except due date
Insights:
The high percentage of "Exact" matches suggests duplicate entry is the primary issue. This could result from:
- Invoices being entered in multiple systems without proper synchronization
- Users manually entering invoices that were already imported via EDI or API
- Batch import processes running multiple times without duplicate checking
The "Invoice Amount Change" pattern often indicates legitimate invoice corrections. For example:
- A vendor sends an invoice for $5,000
- An error is discovered and a corrected invoice for $4,850 is sent
- Both invoices exist in the system with the same invoice number
These require investigation but may not be true duplicates. The original invoice should be voided rather than simply flagged as a duplicate.
The "Invoice Date Change" pattern may reveal scanning or OCR issues where the same physical invoice is scanned multiple times with slightly different date interpretations.
By grouping duplicates by vendor, you might discover that 80% of duplicates come from just 3 vendors. This suggests targeted solutions like improved EDI integration with those specific vendors or additional validation rules in the invoice entry screen for high-volume suppliers.
This pattern analysis transforms duplicate detection from reactive cleanup into proactive process improvement, helping you address root causes rather than just symptoms.
Output
The calculator produces a summary table with one row per duplicate group, along with three key performance indicators displayed at the top of the output.
Summary Metrics
Total Duplicate Value: The total monetary value of all invoices identified as duplicates across all groups. This metric helps quantify the financial risk exposure from potential duplicate payments. Only calculated when Invoice Amount is specified in settings.
Number of Duplicates: The total count of individual invoice cases flagged as part of duplicate groups. This metric indicates the scope of the duplicate issue in your dataset.
Closest Due Date: The earliest upcoming payment deadline among all duplicate invoices. This metric helps prioritize which duplicates require urgent investigation. Only calculated when Due Date is specified in settings.
Duplicate Groups Table
Each row in the main table represents one group of duplicate invoices:
Group Name: Unique identifier for each set of duplicates, typically combining vendor name and invoice number.
Match Type: Indicates the type of duplicate pattern detected:
- "Exact" - All fields match identically
- "Invoice Amount Change" - Same vendor and invoice number with different amounts
- "Invoice Date Change" - Same vendor, invoice number, and amount with different invoice dates
- "Invoice Due Date Change" - All core fields match with different due dates
Group Count: The number of invoice cases in this duplicate group (e.g., 2 for a simple duplicate, 3+ for multiple entries of the same invoice).
Group Value: The total invoice amount for all invoices in this specific duplicate group.
Resolution Workflow Columns:
- Not A Duplicate: User-marked flag indicating the group was reviewed and determined to be a false positive
- Is Resolved: Indicates whether the duplicate has been investigated and addressed
- Resolved By: The name or identifier of the person who resolved the duplicate
- Resolved Time: The timestamp when the resolution occurred
Visualization Options
The calculator output can be used to create various visualizations:
- KPI Dashboard: Display the three summary metrics (Total Duplicate Value, Number of Duplicates, Closest Due Date) as prominent indicators
- Match Type Breakdown: Create a bar chart showing the distribution of different duplicate types to identify patterns
- Resolution Progress: Build a progress indicator showing the percentage of duplicate groups that have been resolved
- Vendor Analysis: Group results by vendor to identify which suppliers have the most duplicate invoice issues
- Timeline View: Plot duplicate creation dates versus resolution dates to track processing time
This documentation is part of the mindzie Studio process mining platform.