Fraud Detection Algorithm
(Redirected from Data analysis techniques for fraud detection)
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A Fraud Detection Algorithm is a detection algorithm of fraud events.
- Context:
- It can be implemented by a Fraud Detection System (to solve a fraud detection task).
- …
- Counter-Example(s):
- See: Cell Phone, Insurance Claim, Tax Return, Credit Card Fraud.
References
2020
- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Data_analysis_techniques_for_fraud_detection#Introduction Retrieved:2020-10-21.
- Fraud that involves cell phones, insurance claims, tax return claims, credit card transactions, government procurement etc. represent significant problems for governments and businesses and specialized analysis techniques for discovering fraud using them are required. These methods exist in the areas of Knowledge Discovery in Databases (KDD), Data Mining, Machine Learning and Statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes. In general, the primary reason to use data analytics techniques is to tackle fraud since many internal control systems have serious weaknesses. For example, the currently prevailing approach employed by many law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. As a result, a large number of fraud cases remain undetected and unprosecuted. In order to effectively test, detect, validate, correct error and monitor control systems against fraudulent activities, businesses entities and organizations rely on specialized data analytics techniques such as data mining, data matching, sounds like function, Regression analysis, Clustering analysis and Gap. Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence.