Smart Invoice Matching for Suppliers: Automate Your Accounts Payable Process with Python

In many businesses, processing supplier invoices involves manually matching them with purchase orders (POs), which can be time-consuming and prone to errors. By automating this process with Python, you can significantly speed up invoice matching, reduce human error, and improve overall efficiency in the accounts payable process.

Benefits of Smart Invoice Matching

• Faster Processing: Automate invoice matching to speed up the approval and payment cycle.
• Reduced Errors: Minimize human error in matching invoices with purchase orders or receipts.
• Increased Accuracy: Ensure that invoices match the agreed-upon terms in POs (e.g., quantity, price, and delivery).
• Improved Cash Flow: Streamline the approval and payment process to improve your company’s cash flow.
• Cost Savings: Free up resources from manual processing, allowing your team to focus on higher-value tasks.

How Python Can Automate Invoice Matching

Python can automate the process of matching invoices to purchase orders using the following steps:

1. Extract Invoice and Purchase Order Data
2. Match Invoice with Purchase Order
3. Flag Discrepancies
4. Generate Reports for Finance Team
5. Send Notifications for Approval or Payment

Step 1: Extracting Data from Invoices and Purchase Orders

Typically, invoices and purchase orders are received in formats such as PDF, Excel, or CSV. You can use Python libraries to extract relevant data from these documents.

For invoices, libraries such as pdfplumber or PyPDF2 can extract data from PDF files, while pandas can be used to handle CSV and Excel files.

Example code to extract data from CSV files (for simplicity, we’ll use CSV for both invoices and POs):

import pandas as pd

# Load the invoice and PO data
invoice_df = pd.read_csv('invoices.csv')  # Invoice data with columns: 'Invoice Number', 'Supplier', 'Amount', 'PO Number', etc.
po_df = pd.read_csv('purchase_orders.csv')  # PO data with columns: 'PO Number', 'Supplier', 'Amount', 'Quantity', etc.

# Example invoice data
print(invoice_df.head())
print(po_df.head())

Step 2: Matching Invoices with Purchase Orders

The next step is to match invoices to purchase orders based on key data fields, such as the invoice number, PO number, and amount. You can use pandas to perform this matching.

Example code to match invoices with POs:

# Merge the data on 'PO Number' and 'Supplier' to match the invoice with the PO
merged_df = pd.merge(invoice_df, po_df, how='inner', on=['PO Number', 'Supplier'])

# Check if the invoice amount matches the purchase order amount
merged_df['Amount Match'] = merged_df['Amount_x'] == merged_df['Amount_y']

# Display matched invoices
matched_invoices = merged_df[merged_df['Amount Match'] == True]
print("Matched Invoices:\n", matched_invoices)

In this code, we use pd.merge to combine the invoice and purchase order data based on the PO number and supplier. Then, we compare the invoice amount with the purchase order amount to see if they match.

Step 3: Flagging Discrepancies

If there is a mismatch between the invoice and purchase order (e.g., a price or quantity discrepancy), Python can automatically flag these invoices for review.

Example code to flag discrepancies:

# Flag discrepancies based on amount or quantity mismatches
merged_df['Discrepancy'] = merged_df['Amount_x'] != merged_df['Amount_y']

# Display invoices with discrepancies
discrepancy_invoices = merged_df[merged_df['Discrepancy'] == True]
print("Invoices with Discrepancies:\n", discrepancy_invoices)

This code creates a new column Discrepancy that marks invoices with mismatches in the amount or quantity.

Step 4: Generating Reports for the Finance Team

Python can automatically generate a summary report of matched invoices and flagged discrepancies. You can save this report to a CSV file or generate a detailed PDF report using libraries like ReportLab.

Example code to generate a report:

# Save the report to a CSV file
matched_invoices.to_csv('matched_invoices.csv', index=False)
discrepancy_invoices.to_csv('discrepancy_invoices.csv', index=False)

# Alternatively, generate a simple PDF report
from fpdf import FPDF

def generate_pdf_report(matched, discrepancies):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font('Arial', 'B', 16)

    pdf.cell(200, 10, txt="Matched Invoices", ln=True)
    pdf.set_font('Arial', '', 12)
    for index, row in matched.iterrows():
        pdf.cell(200, 10, txt=f"Invoice {row['Invoice Number']} - Amount: {row['Amount_x']}", ln=True)

    pdf.add_page()
    pdf.cell(200, 10, txt="Invoices with Discrepancies", ln=True)
    for index, row in discrepancies.iterrows():
        pdf.cell(200, 10, txt=f"Invoice {row['Invoice Number']} - Discrepancy: {row['Discrepancy']}", ln=True)

    pdf.output('invoice_report.pdf')

# Example usage: generate a report for matched and discrepancy invoices
generate_pdf_report(matched_invoices, discrepancy_invoices)

This code creates a PDF report listing matched invoices and invoices with discrepancies.

Step 5: Sending Notifications for Approval or Payment

If discrepancies are found or invoices need approval, Python can automatically send email notifications to the finance team or relevant personnel for review.

Example code to send notifications using smtplib:

import smtplib
from email.mime.text import MIMEText

def send_email(to_email, subject, message):
    """Send an email notification"""
    msg = MIMEText(message)
    msg['Subject'] = subject
    msg['From'] = 'your_email@example.com'
    msg['To'] = to_email

    with smtplib.SMTP('smtp.gmail.com', 587) as server:
        server.starttls()
        server.login('your_email@example.com', 'your_password')
        server.sendmail('your_email@example.com', to_email, msg.as_string())
    print(f"Email sent to {to_email}.")

# Example: Send email about an invoice discrepancy
send_email('finance_team@example.com', 'Discrepancy Found in Invoice #12345', 'There is a discrepancy in Invoice #12345. Please review the details.')

This script sends an email notification about discrepancies, enabling quick action from the finance team.

Conclusion

Automating the invoice matching process with Python can significantly reduce the time spent manually processing invoices, minimize errors, and enhance accuracy. By integrating Python with accounting systems, you can automate data extraction, invoice matching, discrepancy flagging, report generation, and notifications, making your accounts payable process more efficient and accurate. With this streamlined approach, businesses can ensure faster payments, better cash flow management, and improved vendor relationships.