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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. Artificial intelligence in fraud detection - Wikipedia

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

    The ability of machine learning and deep learning to swiftly and effectively sort through vast volumes of data in the forms of various documents relevant to companies and documents being audited makes them applicable to the domains of audit and fraud detection. Examples of this include recognizing key language in contracts, identifying levels ...

  4. Data analysis for fraud detection - Wikipedia

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

    Relatively rare events such as fraud may need to be over sampled to get a big enough sample size. [10] These manually classified records are then used to train a supervised machine learning algorithm. After building a model using this training data, the algorithm should be able to classify new records as either fraudulent or non-fraudulent.

  5. SAS (software) - Wikipedia

    en.wikipedia.org/wiki/SAS_(software)

    SAS (previously "Statistical Analysis System") [1] is a statistical software suite developed by SAS Institute for data management, advanced analytics, multivariate analysis, business intelligence, criminal investigation, [2] and predictive analytics.

  6. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. [4] The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome.

  7. Choice modelling - Wikipedia

    en.wikipedia.org/wiki/Choice_modelling

    Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or contexts. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items (A, B and C) on some relevant latent scale (typically ...

  8. Cynefin framework - Wikipedia

    en.wikipedia.org/wiki/Cynefin_framework

    The Cynefin framework (/ k ə ˈ n ɛ v ɪ n / kuh-NEV-in) [1] is a conceptual framework used to aid decision-making. [2] Created in 1999 by Dave Snowden when he worked for IBM Global Services, it has been described as a "sense-making device". [3] [4] Cynefin is a Welsh word for 'habitat'. [5]

  9. Fraud deterrence - Wikipedia

    en.wikipedia.org/wiki/Fraud_deterrence

    Fraud deterrence is based on the premise that fraud is not a random occurrence; fraud occurs where the conditions are right for it to occur. Fraud deterrence attacks the root causes and enablers of fraud; this analysis could reveal potential fraud opportunities in the process, but is performed on the premise that improving organizational procedures to reduce or eliminate the causal factors of ...