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Mitigating Spreadsheet Model Risk with Python Open Source Infrastructure Oliver BeaversDirector, Trivium Financial Group Oliver×triviumfinancialgroup.com 969 2nd St. SE, Charlottesville, VA 22902ABSTRACT Across
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01
Begin by identifying the potential risks associated with the spreadsheet model.
02
Carefully determine the important inputs and assumptions that the model relies on.
03
Implement adequate error-checking mechanisms to minimize user input errors or formula mistakes.
04
Regularly update the model to reflect any changes in underlying data or business processes.
05
Conduct thorough testing and validation to ensure the accuracy and reliability of the model's outputs.
06
Document all the steps taken to mitigate risks, including any assumptions made or limitations encountered.
07
Establish a review process involving subject matter experts to validate the model and provide feedback.
08
Communicate the risks associated with the spreadsheet model to relevant stakeholders and ensure they understand the limitations.
09
Continuously monitor and assess the model's performance to identify and address any emerging risks or issues.
10
Consider implementing alternative models or tools to complement or validate the spreadsheet model results.

Who needs mitigating spreadsheet model risk?

01
Financial institutions
02
Risk management departments
03
Finance professionals
04
Business analysts
05
Auditors
06
Regulatory authorities
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Mitigating spreadsheet model risk involves identifying and reducing potential errors or inaccuracies in spreadsheet models that could result in negative consequences.
Individuals or organizations who use spreadsheet models for decision-making processes are required to file mitigating spreadsheet model risk.
Mitigating spreadsheet model risk can be filled out by conducting thorough reviews, testing for accuracy, implementing controls, and documenting assumptions.
The purpose of mitigating spreadsheet model risk is to ensure the reliability and integrity of spreadsheet models used in critical decision-making processes.
Information such as assumptions made, data sources used, model methodologies, testing procedures, and validation results must be reported on mitigating spreadsheet model risk.
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