The impact of using artificial intelligence on audit quality in combating financial fraud: A field study on Sudanese commercial banks
DOI:
https://doi.org/10.59992/IJFAES.2026.v5n6p5Keywords:
Artificial Intelligence, Audit Quality, Financial Fraud, Sudanese Banks, Machine Learning, Genetic Algorithms, Intelligent Agents, Artificial Neural NetworksAbstract
This study aimed to measure the impact of artificial intelligence techniques, with their four dimensions: Genetic Algorithms, Intelligent Agents, Artificial Neural Networks, and Machine Learning, on Audit Quality in combating Financial Fraud in Sudanese commercial banks operating in Khartoum State during 2023-2024.Adopting the descriptive analytical approach, primary data were collected via a questionnaire distributed to 142 internal auditors and risk managers from 15 commercial banks. Data were analyzed using SPSS 26 through descriptive statistics, Cronbach's Alpha, Pearson correlation, and multiple/stepwise regression.
Results revealed a statistically significant impact at α ≤ 0.05 of AI dimensions combined on combating financial fraud, with R = 0.771 and R² = 0.594, explaining 59.4% of variance. Machine Learning ranked first individually with 46.7% explanatory power. The study recommends investing in human capital, ensuring data quality, building tech partnerships, and establishing AI units in banks to enhance fraud detection.
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