Fraud Analysis
Fraud analytics is where data is used via various analysis techniques to avoid different variations of financial fraud. More specifically, it can help organisations predict and therefore prevent future fraudulent behavior, which can then help them quickly detect fraudulent activity in the present time.
The process of fraud analytics requires collecting and accumulating the appropriate data and studying it for certain inconsistencies and anomalies. Once results have come through, they are converted into different understandings which can allow companies to handle potential fraudulent threats before they happen. This can also allow companies to create a fraud detection method.
IntelliQ uses Forensic Analytics, which can quickly identify exceptions and uncover hidden behavior that suggests fraud is being committed.
FAQs
How do you do a fraud analysis?
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To identify the different areas where fraud can arise, a profile will need to be set up.
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Once this is made, evaluate the risk of fraud in the particular area within the company.
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Finally, follow Ad-hoc testing process which will help find signs of potential fraud in your company.
Which algorithm is used for fraud detection?
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Logistic Regression is used as a managed learning method once the conclusion is final. The aim is to distinguish whether the outcome will be fraud or non-fraud once a transaction happens.
How do you determine is a transaction is fraudulent?
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Assessing each transaction will help identify if there are any chances for fraud to occur. By having a hypothesis, this will assess and discover if there is any potential fraud going on, which you can then examine further.
What is a fraud detection system?
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A fraud detection system can considerably decrease the possibility of fraud happening in the company.
Updated on 06/09/2022