ATM Fraud Detection Using Outlier Detection

被引：3

作者：

Laimek, Roongtawan ^{[1
]}

Kaothanthong, Natsuda ^{[1
]}

Supnithi, Thepchai ^{[2
]}

机构：

[1] Thammasat Univ, Sirindhorn Int Inst Technol, Bangkok, Thailand

[2] Natl Elect & Comp Technol Ctr NECTEC, Language & Semant Technol LST Lab, Khlong Neung, Thailand

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2018, PT I | 2018年 / 11314卷

关键词：

ATM fraud; Data mining; Outlier detection;

D O I：

10.1007/978-3-030-03493-1_56

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A fraud detection model that applies accounts' behavior features and outlier detection methods are proposed. Given a set of transactions, the accounts are grouped into two groups according to the location that the transactions took place, i.e., 'local-only' and `has-abroad'. A feature is extracted to reflect the normal behavior of the accounts in each group. Only known legitimate transactions are used to extract a set of features for representing a legitimate behavior. An unknown transaction is classified either normal or fraud using an outlier detection. The experimental result shows that the proposed feature with an Isolation Forest outlier detection technique is able to detect all fraud transactions.

引用

页码：539 / 547

页数：9

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