Solving Fraud Detection Tasks Based on Wavelet-Neuro Autoencoder

Autor:	Polina Zhernova, Olena Vynokurova, Dmytro Peleshko, Andrii Kovalenko, Iryna Perova
Rok vydání:	2020
Předmět:	Computer science business.industry Business process Decision tree Behavioral pattern Customer relationship management computer.software_genre Autoencoder Field (computer science) Set (abstract data type) Financial transaction Data mining business computer
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783030542146 ISDMCI
DOI:	10.1007/978-3-030-54215-3_34
Popis:	The basis of any business is customer databases, which provide information on customer relations with the company. For example, in the field of banking services, the database stores information about the client, account number, data on financial transactions in online trading, purchased goods, their quantity, time of purchase, etc. A fraud detection is a field of data mining, which includes a set of methods for detecting fraudulent activities in the credit and financial sector, telecommunications, and other areas where illegal manipulations with customer accounts, tariff changes, etc. are possible. Typically, a fraud detection technique is based on the detection of events that do not fit into a specific pattern or behavioral pattern specific to a given business process or client that does not correspond to its patterns and trends. Analytical methods of Data Mining are widely used to build fraud detection systems: neural networks, decision trees, associative rules, sequential patterns, etc.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::af0841bcdf71ed9f3a46ca24095f450f https://doi.org/10.1007/978-3-030-54215-3_34 Zobrazit plný text záznamu