46 ALRAQABA . ISSUE 20 as the likelihood of fraud, allows auditors to prioritize their efforts effectively. Additionally, ML can handle missing data points through predictive modeling, ensuring comprehensive analysis. This technology›s strength lies in its ability to process diverse data types, including structured , semistructured , and unstructured information, providing a holistic view of the financial landscape. One of ML›s greatest strengths is its ability to uncover causal relationships between variables, helping auditors understand the root causes of financial irregularities. ML can be used without requiring in-depth domain expertise, allowing auditors to leverage its power without extensive financial knowledge. While ML models require careful development to mitigate bias and overfitting, when implemented correctly, they can significantly enhance audit accuracy and efficiency. In the next section, the author will explore how ML is being utilized in various auditing tasks, highlighting its realworld applications. The Uses and Impact of ML on Auditing ML is revolutionizing the auditing landscape by offering a powerful toolkit to enhance efficiency, accuracy, and transparency in the audit process. By analyzing vast amounts of data, ML algorithms can identify patterns, anomalies, and trends that are often imperceptible to human auditors. Here is how ML is transforming the way audits are conducted. Enhanced Fraud Detection and Risk Assessment One of the most significant applications of ML in auditing is in fraud detection and risk assessment. ML algorithms can analyze historical financial data to identify patterns associated with fraudulent activities. This enables auditors to proactively identify high-risk areas and allocate resources accordingly. By combining ML with traditional audit techniques, auditors can significantly improve the detection rate of fraudulent schemes. High Prediction Accuracy Dealing with Any Type of Data Analysis and with Enormous Data The Power of ML Causal Relationship Discovery by Finding Patterns and Trends Bias and Overfitting Mitigation Minimal Domain Knowledge Requirement Saving Time and Reduced Computational Cost Structured data is data that is organized in a regular format, such as a database. Semi-structured data is data that is partially organized, such as XML or JSON. Unstructured data is data that is not organized in a regular format, such as text or images. Computers Audit and Figure 1: Machine Learning Strength
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