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This research article explores an innovative audit lead tool utilizing artificial neural networks to enhance audit yield predictions based on historical tax data, improving compliance and fairness in tax audits.
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Researchers conducting audits on lead selection yield for publications in PLOS One for the year 2022
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Academic institutions or organizations looking to assess the efficiency of lead selection processes in research studies published in PLOS One
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plos_one_audit_lead_selection_yield_prediction_2022 is a standardized document used for reporting and predicting the yield of audit lead selections in the year 2022.
Organizations and entities involved in the audit process and financial reporting are required to file plos_one_audit_lead_selection_yield_prediction_2022.
To fill out plos_one_audit_lead_selection_yield_prediction_2022, one must provide relevant financial data, performance metrics, and any forecasts regarding the audit leads.
The purpose of plos_one_audit_lead_selection_yield_prediction_2022 is to facilitate better planning and allocation of audit resources through accurate yield predictions.
The information that must be reported includes audit lead details, expected yields, historical data, and any other pertinent financial indicators.
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