ABSTRACT:
Financial fraud, such as money
laundering, is known to be a serious process of crime that makes illegitimately
obtained funds go to terrorism or other criminal activity. This kind of illegal
activities involve complex networks of trade and financial transactions, which
makes it difficult to detect the fraud entities and discover the features of
fraud. Fortunately, trading/transaction network and features of entities in the
network can be constructed from the complex networks of the trade and financial
transactions. The trading/transaction network reveals the interaction between
entities, and thus anomaly detection on trading networks can reveal the
entities involved in the fraud activity; while features of entities are the
description of entities, and anomaly detection on features can re_ect details
of the fraud activities. Thus, network and features provide complementary
information for fraud detection, which has potential to improve fraud detection
performance. However, the majority of existing methods focus on networks or
features information separately, which does not utilize both information. In
this paper, we propose a novel fraud detection framework, CoDetect, which can
leverage both network information and feature information for financial fraud
detection. In addition, the CoDetect can simultaneously detecting financial
fraud activities and the feature patterns associated with the fraud activities.
Extensive experiments on both synthetic data and real-world data demonstrate
the efficiency and the effectiveness of the proposed framework in combating
financial fraud, especially for money laundering.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
DONGXU HUANG ,
DEJUN MU, LIBIN YANG , AND XIAOYAN CAI, “CoDetect: Financial Fraud Detection
With Anomaly Feature Detection”, IEEE ACCESS, 2018.