While diving into the automation of the financial processes, business automation and even compliance, the mix of Excel spreadsheets with Python creates a powerful, flexible system to manage complex business logic – because it allows us, without leaving our existing Excel models behind. In this post, we will take an example and explore how an Excel file can serve not just as a data entry tool, but as a fully functional rule engine – for a backend of a tax computation service.
What is this about?
Excel is used as a data management tool, favored by accountants and business analysts for its flexibility and familiarity. But there if we could use Excel to function like a backend rule engine – for calculating taxes, applying business logic, and delivering outputs as if it were a coded service?
That’s exactly what we’ve going to cover here in this topic. Using Python, the openpyxl and formulas libraries, and a well-structured Excel file (employmentTax.xlsx), we transform Excel into a callable, modifiable logic engine / rule engine for tax calculation backend services.
How It Works: The Concept and Architecture
1. Design the Rule Engine in Excel
The Excel workbook contains a dedicated sheet (named ruleEngine) where each row defines:
• An attribute name (e.g., gross_annual_salary, taxable_income)
• A corresponding formula that encapsulates the business logic
Each of these attributes is named dynamically using Python, allowing us to reference them in code like function arguments or return values.
2. Use Python to Map and Execute the Rules
The Python script performs the following steps:
• Downloads the Excel file dynamically from a URL
• Opens the ruleEngine sheet, reads the formulas, and maps them to named ranges
• Uses the formulas library to load and evaluate the workbook as a Pythonic object
• Accepts JSON input (e.g., gross_annual_salary = 1,200,000) and calculates output attributes such as: taxable_income, total_income_tax, monthly_tds, Different tax slabs
The entire engine can be executed via a function like main_tax_calculator() which accepts input/output specifications as JSON strings – which makes it perfect for APIs and automation tools.
Benefits of Using Excel as a Functional Rule Engine
No Need to Rewrite Business Logic in Code: Business rules remain in Excel – where stakeholders like tax consultants and auditors are most comfortable. Any updates can be made in Excel without touching the backend code.
Seamless Integration: The Python wrapper makes it easy to plug Excel logic into web apps, APIs, or batch processing systems.
Auditable and Transparent: Because the logic lives in Excel formulas, it is visible, traceable, and easy to audit. This is crucial in regulated environments like tax compliance.
Rapid Prototyping: Need to test a new policy rule or a business rule? Just add it to the Excel sheet. No need to modify and redeploy a software application.
Low Technical Barrier: Business teams can manage the rule engine independently of developers, reducing cross-functional bottlenecks.
This method is not limited to tax logic. It is applicable to many domains: Payroll Processing: Handle deductions, benefits, and TDS logic. Loan Eligibility and Scoring: Apply rule-based decision logic for approvals. Insurance Premium Calculation: Customize based on user inputs and policy types. Sales Commission Systems: Calculate variable compensation based on performance metrics. Compliance Checklists: Automate “pass/fail” outputs based on multiple parameters.
In short, any rule-based business process modeled in Excel can be converted into a backend service.
By combining Excel’s flexibility of being used a business rule engine with Python’s integration, we get the best of both worlds: Business users maintain control over logic and Developers gain a reliable, testable, and callable engine. Whether we are automating taxes, benefits, or internal policy enforcement, using Excel as a business rule engine is a powerful opportunity to leverages existing calculation engines meticulously maintained in Excel while reducing development time.
Try it out in a Google Colab
You can access the sample Google Colab file used in this post here and customize it for your own rules. Let Excel do the math and let Python do the orchestration.
