Publication
Machine Learning in Audit of Operations: Predicting the Error Rate of European Funds
| Summary: | In the field of auditing of operations, to assess error rates for Book Values (BV) in a European funds population, Monetary Unit Sampling (MUS), a widely used sampling methodology, enables auditors to extrapolate about the population being materially misstated. The thesis delved into the potential of incorporating supervised Machine Learning (ML) regression models to innovate how error rates are identified in European funds, given the limited exploration of ML’s effectiveness in auditing. Likewise, the study focused on showcasing the capability of predicted error rates align closely with the true misstatement values operations, previously selected through MUS sampling method. A pure model-based conceptual framework approach was developed introducing supervised ML regression models, including tree-based algorithms, such as Decision Trees, eXtreme Gradient Boosting, Categorical Boosting and Random Forest Quantile, and the Multilayer Perceptron algorithm. Additionally, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics, data visualization tools and Repeated Stratified Cross-Validation were used for models’ assessment. Furthermore, data samples were provided by the IGF entity, from training, validation and testing purposes. Results exhibited the proficiency of ML algorithms in predicting specific pattern performances although generalization across error rates remained a challenge. This advancement sets the stage for moving beyond standard the standard MUS method towards Artificial Intelligence (AI) applications in audit of operations. |
|---|---|
| Main Authors: | Silva, Sara Filipa Santos Pereira Fernandes da |
| Subject: | Operational Audits Monetary Unit Sampling Machine Learning Supervised Regression Algorithms SDG 16 - Peace, justice and strong institutions |
| Year: | 2024 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | open access |
| Associated institution: | Universidade Nova de Lisboa |
| Language: | English |
| Origin: | Repositório Institucional da UNL |
| Summary: | In the field of auditing of operations, to assess error rates for Book Values (BV) in a European funds population, Monetary Unit Sampling (MUS), a widely used sampling methodology, enables auditors to extrapolate about the population being materially misstated. The thesis delved into the potential of incorporating supervised Machine Learning (ML) regression models to innovate how error rates are identified in European funds, given the limited exploration of ML’s effectiveness in auditing. Likewise, the study focused on showcasing the capability of predicted error rates align closely with the true misstatement values operations, previously selected through MUS sampling method. A pure model-based conceptual framework approach was developed introducing supervised ML regression models, including tree-based algorithms, such as Decision Trees, eXtreme Gradient Boosting, Categorical Boosting and Random Forest Quantile, and the Multilayer Perceptron algorithm. Additionally, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics, data visualization tools and Repeated Stratified Cross-Validation were used for models’ assessment. Furthermore, data samples were provided by the IGF entity, from training, validation and testing purposes. Results exhibited the proficiency of ML algorithms in predicting specific pattern performances although generalization across error rates remained a challenge. This advancement sets the stage for moving beyond standard the standard MUS method towards Artificial Intelligence (AI) applications in audit of operations. |
|---|