Data Analysis
Data analysis consists of operating with data to gather valuable and practical information which can then be applied to create informed and knowledgeable conclusions. Data analysis can be used in qualitative research which includes statistical procedures and can also be a part of a constant interactive process whereby data is always collected and analysed at the same time.
For fraud prevention, data analysis is used to determine anomalies, it does not directly detect fraud. However, after data has been investigated and confirmed, an audit can then evaluate if certain transactions are fraudulent.
By using IntelliQ's Data Challenge, we utilise your EPOS data and reveal examples of visible theft in your business.
FAQs
What is fraud analysis?
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Fraud analysis revolves around the method of working with data to utilise material, which can be used to make strategic conclusions.
How is data detected in fraud?
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Fraud is detected by using data mining which organizes, and categorizes data in order to locate connections and regulations in the data that may indicate different patterns, as well as fraud related patterns.
How can data analytics prevent fraud?
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Fraud can be prevented via data analytics by identifying anomalies within a pattern. In order to succeed, a specialist determines a baseline of non-fraudulent activity to differentiate the questionable data.
How do you do fraud analysis?
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Set up a profile indicating where fraud may be likely to show up, including the different forms of fraud that may occur.
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Assess the likelihood of fraud being exposed to the organization.
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Proceed to using Ad-hoc testing method in order to find anomaly's within the organization.
Updated on 09/08/2022