Financial model Reviewed and bugs fixed

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What Should You Look for When Reviewing a Financial Model?
A single error in any cell of your Excel financial model can throw off your whole calculation. For example, an extra zero or omission of a decimal point, or a wrong formula reference can mess up everything from your assumptions to risk profiles.
A thorough review of your financial model can help you identify any inaccuracies. I will Look for the following when reviewing your financial model:
• Calculation Errors: Check for typos and incorrect input values.
• Incorrect Assumptions: Make sure the assumptions are based on accurate data and reflect the latest financial situation.
• Financial Statement Linkages: Identify any potential inconsistencies in the relationships between different financial statements.
• Data Quality: Ensure the data used in the model is accurate and up-to-date.
• Inaccurate Outputs: Check the figures and projections to ensure they accurately reflect the data used in the model.

While reviewing, I will consider the purpose of the financial model and how it will be used. It can help you verify that the equations and assumptions accurately reflect the desired outcome.
Steps in Reviewing a Financial Model
Review the Inputs and Assumptions
It includes checking data sources, understanding the nature and scope of information that is input into the model, validating all formulas used, and ensuring the accuracy of any numbers or assumptions. The modeler should also check that all the assumptions underlying the calculations are reasonable and consistent with industry practices.
Review the Outputs
The next step involves reviewing the outputs generated by the model. The modeler should examine the sensitivity of the results to changes in inputs and understand whether any key relationships are being captured. Additionally, the modeler should look for any inconsistencies or strange patterns that may indicate an error in the calculations.
Validate the Model
In this step, the modeler should validate the model against real-world data and compare it with other models. For example, the modeler should compare the results of the model with a set of benchmarks that reflect industry or market standards.

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