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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.
SAS 99 defines fraud as an intentional act that results in a material misstatement in financial statements. There are two types of fraud considered: misstatements arising from fraudulent financial reporting (e.g. falsification of accounting records) and misstatements arising from misappropriation of assets (e.g. theft of assets or fraudulent expenditures).
The audit firm Ernst & Young has posited these claims by declaring that their deep learning systems have been used to reduce time spent on administrative tasks by analyzing relevant audit documents. According to the firm, this has allowed their employees to focus more on judgement and analysis.
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.
Data analytics can also provide a thorough, detailed analysis of a company's general ledger or sub ledgers, which can provide more evidence to the auditor. [13] In relation to specific kinds of audit evidence, there are a couple examples where audit data analytics can alter the methods of collection.
Forensic data analysis (FDA) is a branch of digital forensics. It examines structured data with regard to incidents of financial crime. The aim is to discover and analyse patterns of fraudulent activities. Data from application systems or from their underlying databases is referred to as structured data.
The role and the responsibilities of the audit committee, in general terms, are to: (a) Discuss with management, internal and external auditors and major stakeholders the quality and adequacy of the organization's internal controls system and risk management process, and their effectiveness and outcomes, and meet regularly and privately with ...
The types of questions (e.g.: closed, multiple-choice, open) should fit the data analysis techniques available and the goals of the survey. The manner (random or not) and location (sampling frame) for selecting respondents will determine whether the findings will be representative of the larger population .