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Technology that works with big data can work alongside audit evidence to increase the quality and efficiency of an audit. Big data uses pattern recognition, natural-language processing, and data mining to elevate audit data analytics, [2] which is briefly discussed in the paragraph below.
Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: . Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.
Data auditing is the process of conducting a data audit to assess how company's data is fit for given purpose. This involves profiling the data and assessing the impact of poor quality data on the organization's performance and profits.
The focus is on innovative research in data mining, knowledge discovery, and large-scale data analytics. Papers emphasizing theoretical foundations are particularly encouraged, as are novel modeling and algorithmic approaches to specific data mining problems in scientific, business, medical, and engineering applications.
Audit technology is a general term used for computer-aided audit techniques (CAATs) used by accounting firms to enhance an engagement. These techniques improve the efficiency and effectiveness of audit findings by allowing auditors to analyze much larger sets of data, sometimes using entire populations of data, rather than taking a sample.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
Process mining is a family of techniques for analyzing event data to understand and improve operational processes. Part of the fields of data science and process management, process mining is generally built on logs that contain case id, a unique identifier for a particular process instance; an activity, a description of the event that is occurring; a timestamp; and sometimes other information ...