Файл	Ном	Сабт карда шуд
Numonova Nigora Rustamovna	LITERATURE REVIEW FOR FINANCIAL FRAUD DETECTION	33

Authors:  Numonova Nigora Rustamovna - The Assistant of the Digital Economy Department at KPITTU

JOURNAL NUMBER: 3(62). YEAR OF ISSUE: 2022. LANGUAGE OF THE ARTICLE: Russian

ANNOTATION

With the rise and rapid growth of e-commerce, there has also been an increase in financial scams associated with it, which annually lead to the theft of billions of dollars around the world. Fraud detection involves closely examining the behavior of groups of users in order to roughly identify, detect, or prevent unwanted behavior. Unwanted behavior is a broad term that includes offenses such as fraud, infringement, and account evasion. In fact, fraudulent transactions are interspersed with genuine transactions, and simple pattern matching methods are often not enough to accurately detect such fraud. In this review, we will focus on the classification of fraudulent behavior, identifying the main sources and characteristics of data, based on which fraud detection was carried out. This paper provides a comprehensive review and overview of the various financial fraud detection methods used in various types of fraud such as credit card fraud detection, online auction fraud, telecommunications fraud detection, and computer intrusion detection.

KEY WORDS

financial fraud, data mining, neural network, fraud detection.

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