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Fraud

Fraud is intentional deception or unauthorized activity undertaken to obtain an improper benefit or cause another party a loss.

How 2 Manning books explain it

Financial AI in Practice

Fraud is intentional deception intended to secure an unfair or unlawful gain or cause another party a loss.

How it is used
Frames illicit exploitation of financial products and transactions as a detection and prevention problem for banking, financial services, and insurance.
How deep it goes
Defines the scope, classifies six attack vectors, and outlines four defense layers, but supplies no implementation code in the provided evidence.
Examples
Uses stolen card purchases, refund abuse, account takeover, synthetic identities, false loan income, and employee embezzlement to distinguish attack vectors.
What you can do after
Evaluate/understand it by classifying a financial fraud attempt and identifying relevant data, detection methods, and operational responses.

Read how Financial AI in Practice explains fraud →

Outlier Detection in Python

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

How it is used
Motivates outlier detection as a practical check for financial loss, reputational risk, and concealed corporate misconduct.
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.

Read how Outlier Detection in Python explains fraud →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.