The tax authorities working for various governments are often confronted with the challenge of detecting tax evasion and fraud. Imagine you work at the income tax department. All residents and businesses of the country earning income should file tax information electronically along with their tax payment.
- How could you use data mining concepts to help detect fraud?
- What kinds of fraud can you detect?
- Explicitly state your assumptions.
- Purely by data mining techniques, can you confirm that an individual has committed fraud? Provide reasons.
Fraud detection is a data analytic process. In other words, any indication of fraud might be revealed by comparing with other relevant data. One example of tax fraud is when the tax payer shows extra-ordinarily high business or work expenses to offset against their income. This is an example of fraud that can be detected using data mining techniques such as anomaly detection. An anomalous event is defined as an event that occurs rarely. An anomalous event can be detected from a dataset using data outlier detection ...
The discussion explains how data mining techniques can be effectively used to detect fraud and anomalies in financial transactions.
Proactive, Inductive, and Deductive Fraud Detection
Provide an example of each of the following:
- Proactive fraud detection
- Inductive fraud detection
- Deductive fraud detection