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These assertions are relevant to auditors performing a financial statement audit in two ways. First, the objective of a financial statement audit is to obtain sufficient appropriate audit evidence to conclude on whether the financial statements present fairly, in all material respects, the financial position of a company and the results of its ...
The auditor uses assertions in assessing risks by considering potential misstatements that may occur, and thereby designing audit procedures that are responsive to the particular risks. Assertions used by the auditor fall into the following categories: (a) Assertions about classes of transactions and events for the period ended: Occurrence
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.
For example, an auditor may: physically examine inventory as evidence that inventory shown in the accounting records actually exists (existence assertion); inspect supporting documents like invoices to confirm that sales did occur (occurrence); arrange for suppliers to confirm in writing the details of the amount owing at balance date as evidence that accounts payable is a liability (rights ...
Audit log: Specifies whether the product logs activity performed by the user (the auditor) for later reference (e.g., inclusion into audit report). Data graph: Specifies whether the product provides graphs of results. Export (CSV): Specifies whether the product support exporting selected rows to a comma-separated values formatted file.
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.
KNIME (/ n aɪ m / ⓘ), the Konstanz Information Miner, [2] is a free and open-source data analytics, reporting and integration platform.KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept.