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AI-Powered Fraud Detection in Auditing Using Machine Learning and Deep Learning Techniques

Hisham Ahmed Mahmoud

2025enfinancial fraudauditingmachine learningdeep learningfraud detectionclassification models

Abstract

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Financial fraud threatens the transparency and integrity of financial systems, requiring more advanced detection methods than traditional manual auditing. This study applies artificial intelligence, specifically machine learning (Logistic Regression, Random Forest, XGBoost) and deep learning (Convolutional Neural Network), to detect fraudulent transactions using a simulated dataset of 100 labeled financial records. After preprocessing steps including normalization and categorical encoding, the data were split into training and test sets using an 80:20 ratio. Model performance was evaluated with accuracy, precision, recall, F1-score, and ROC-AUC, along with confusion matrix analysis. Among the models, XGBoost achieved the best results, with 95% accuracy and an F1-score of 0.93 for the fraud class, demonstrating excellent fraud detection with minimal misclassification. The findings highlight the effectiveness of ensemble and deep learning techniques as decision-support tools for auditors and real-time financial monitoring systems.

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Cite This Work

@article{13dc865b-3d7e-4dd0-9351-11988536249a,
  title={AI-Powered Fraud Detection in Auditing Using Machine Learning and Deep  Learning Techniques  },
  author={Hisham Ahmed Mahmoud},
  year={2025},
  language={en}
}
TY  - JOUR
TI  - AI-Powered Fraud Detection in Auditing Using Machine Learning and Deep  Learning Techniques  
AU  - Hisham Ahmed Mahmoud
PY  - 2025
LA  - en
ER  -

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