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  2. Statement on Auditing Standards No. 99: Consideration of Fraud

    en.wikipedia.org/wiki/Statement_on_Auditing...

    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).

  3. Data analysis for fraud detection - Wikipedia

    en.wikipedia.org/wiki/Data_analysis_for_fraud...

    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.

  4. Data auditing - Wikipedia

    en.wikipedia.org/wiki/Data_auditing

    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.

  5. Forensic data analysis - Wikipedia

    en.wikipedia.org/wiki/Forensic_data_analysis

    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.

  6. Artificial intelligence in fraud detection - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_in...

    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.

  7. Audit evidence - Wikipedia

    en.wikipedia.org/wiki/Audit_evidence

    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.

  8. Forensic accounting - Wikipedia

    en.wikipedia.org/wiki/Forensic_accounting

    Today, forensic accountants work closely with data analytics to dig through complex financial records. Data collection is an important aspect of forensic accounting because proper analysis requires data that is sufficient and reliable. [24] Once a forensic accountant has access to the relevant data, analytic techniques are applied.

  9. Forensic accountant - Wikipedia

    en.wikipedia.org/wiki/Forensic_accountant

    Forensic accountants need to have a great deal of access to information regarding the company they are investigating or assisting. The information will determine how much a person actually makes, the worth of a business, if there has been fraudulent activity, who committed the fraud, everyone involved, how much was taken from the company, where the money went, and how much can be recovered.