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Codetect: Financial Fraud Detection With Anomaly Feature Detection

This document proposes a framework called CoDetect for detecting financial fraud through anomaly detection on both transaction networks and entity features. CoDetect leverages both network and feature information simultaneously to identify fraudulent activities and patterns. Experimental results on synthetic and real-world data show that CoDetect is effective at combating money laundering fraud. The system requirements for implementing CoDetect include a Pentium Dual Core system with 1 GB RAM, 120 GB hard disk, keyboard, mouse, and Windows 7 operating system.

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97 views2 pages

Codetect: Financial Fraud Detection With Anomaly Feature Detection

This document proposes a framework called CoDetect for detecting financial fraud through anomaly detection on both transaction networks and entity features. CoDetect leverages both network and feature information simultaneously to identify fraudulent activities and patterns. Experimental results on synthetic and real-world data show that CoDetect is effective at combating money laundering fraud. The system requirements for implementing CoDetect include a Pentium Dual Core system with 1 GB RAM, 120 GB hard disk, keyboard, mouse, and Windows 7 operating system.

Uploaded by

The Futura Labs
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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CoDetect: Financial Fraud Detection With

Anomaly Feature Detection

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: 

 System : Pentium Dual Core.


 Hard Disk : 120 GB.
 Monitor : 15’’ LED
 Input Devices : Keyboard, Mouse
 Ram : 1 GB

SOFTWARE REQUIREMENTS: 

 Operating system : Windows 7.


 Coding Language : JAVA/J2EE
 Tool : Netbeans 7.2.1
 Database : MYSQL

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