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Fraud

As explained in Outlier Detection in Python

Financial fraud is the deliberate deception or misrepresentation of financial activities, records, or positions to obtain an improper benefit.

Financial fraud is deliberate deception or misrepresentation of financial activities, records, or positions for improper benefit. It includes losses caused by employees, suppliers, or customers and higher-level misrepresentation by senior management or the company itself. section 1.1.1

Distinguish fraud by its target and operating level:

  • Lower-level fraud bilks a company of money or goods, sometimes through collusion between staff and suppliers or customers. section 1.1.1
  • Higher-level fraud misrepresents a company’s financial position to defraud investors, banks, or potential buyers. section 1.1.1
  • Fraud detection overlaps with tax fraud, insurance fraud, anti-money laundering, and other outlier-detection applications. section 1.1.1

Why detection matters

A company may lose revenue through employee or supplier fraud, while senior managers may inflate earnings to influence stock prices or earn bonuses. Outlier detection provides a way to check for suspicious activity, although detected fraud is often unreported. section 1.1.1

How Outlier Detection in Python covers it

How deep it goes
Distinguishes lower- and higher-level fraud and motivates detection, but stops short of detailing specific detection procedures.
Examples
Uses staff collusion, financial-position misrepresentation, earnings inflation, and bonuses tied to stock prices as examples.
What you can do after
Apply outlier detection when checking financial activity for potentially fraudulent cases.
How it connects
Connects fraud with outlier detection, tax fraud, insurance fraud, anti-money laundering, and securities fraud.

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This explainer was generated by Manning's knowledge graph from the text of Outlier Detection in Python. The section links point to the parts of the book it draws on.