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  2. 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.

  3. 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).

  4. Artificial intelligence in fraud detection - Wikipedia

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

    It is widely used in the financial sector, especially by accounting firms, to help detect fraud. In 2022, PricewaterhouseCoopers reported that fraud has impacted 46% of all businesses in the world. [1] The shift from working in person to working from home has brought increased access to data.

  5. Audit technology - Wikipedia

    en.wikipedia.org/wiki/Audit_technology

    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.

  6. Information technology audit - Wikipedia

    en.wikipedia.org/wiki/Information_technology_audit

    An IT audit is different from a financial statement audit.While a financial audit's purpose is to evaluate whether the financial statements present fairly, in all material respects, an entity's financial position, results of operations, and cash flows in conformity to standard accounting practices, the purposes of an IT audit is to evaluate the system's internal control design and effectiveness.

  7. Audit evidence - Wikipedia

    en.wikipedia.org/wiki/Audit_evidence

    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.

  8. Information security audit - Wikipedia

    en.wikipedia.org/wiki/Information_security_audit

    The auditor should plan a company's audit based on the information found in the previous step. Planning an audit helps the auditor obtain sufficient and appropriate evidence for each company's specific circumstances. It helps predict audit costs at a reasonable level, assign the proper manpower and time line and avoid misunderstandings with ...

  9. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    ELKI is an open-source Java data mining toolkit that contains several anomaly detection algorithms, as well as index acceleration for them. PyOD is an open-source Python library developed specifically for anomaly detection. [56] scikit-learn is an open-source Python library that contains some algorithms for unsupervised anomaly detection.