An efficient money Laundery detection using Spatio temporal correlation based association rule pattern

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Author: 
Dr. Vikas Jayasree
Subject Area: 
Physical Sciences and Engineering
Abstract: 

The implementation of data mining techniques for money laundering fraud detection follow traditional information flow of data mining, which starts with feature selection followed by representation, data collection, management, and performance evaluation. Data mining methods have the potentiality for detecting money laundering fraud in banking as they use past cases of fraud to build models, which identify and detect the risk of fraud.With the increased volume of crime datasets and complexity of relationships between these kinds of data, several data mining approaches were presented that involved anomaly detection using principal component analysis and self organizing map. But nevertheless, with high dimension data, they pose serious issues. In this work, to handle high dimensional data with multi-clustering structure, an Efficient Association Rule Pattern based Money Laundering Detection (EARM-MLD) framework is developed. The association rule pattern mining in EARM-MLD framework consists of three major parts. The first part finds frequent large itemsets from banking rules which have support and confidence values more than a threshold number of times. This in turn reduces the time taken for detecting money laundering. The second part is to construct association rules based on spatio temporal model from those large itemsets to easily perform the detection operation and to integrate it with multi clustering algorithm with the objective of reducing the false positive rate. Finally, the multiclustering algorithm involves the set of money transfer group which fulfills the criteria such as row condition, gathering amounts of money to a single account with minimum set size. The multi cluster elements integrated with EARM-MLD framework are treated as suspected operations which operate in money laundering detection work.The money laundering detection in banking system using EARM is experimented on factors such as time for detecting money laundering,false positive rate, scalability, system efficiency ratio, fraud identification accuracy, number of transaction , number of money transfers. Experimental analysis shows that EARM-MLD framework is able to reduce the time for detecting money laundering by 40.14% and reduce the false positive rate by 18.55% compared to the state-of-the-art works.

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Editorial Board Member's

Dr.Geetanjali Joshi Mishra
Lucknow, UP, INDIA
Dr. Sankalp Verma
India
Tannu Arora
India
Prof Dr. Lee Mun Seng
Malaysia
Dr. Muhammad shoaib Ahmedani
Saudi Arabia
Dr. Lim Gee Nee
Malaysia
DANA MOHAMMED
UK
Ali Majnoony Tootakhaneh
Iran
Prof. Somchai Amornyotin
Thailand
Nurhodja Akbulaev
Turkey
Muhammad Hamid
Pakistan
MOHAMMAD GHOLAMI
Iran
MAVLYANOV ISKANDAR RAKHIMOVICH
Uzbekistan
Hasan Ali Abed Al-Zu’bi
Jordan
Faraz Ahmed Farooqi
Saudi Arabia
Eric Randy Reyes Politud
Philippines
Eapen, Asha Sarah
USA
Dr. Ruchika Khanna
India
DR. MUHAMMAD ISMAIL MOHMAND
UK
DR. MAHESH SHIVAJI CHAVAN
India
Dr. Lokesh Gambhir
India
DR. IRAM BOKHARI
Pakistan
Dr. FARHAT NAZ RAHMAN
Pakistan
Dr. Charu Bisaria
India
Dr. Achmad Choerudin
Indonesia
Bensafi Abd-El-Hamid
Algeria
Dr. Pralhad Kanhaiyalal Rahangdale
India
Dr. ASHWANI KUMAR DUBEY
India
Dr. Arun Kumar
India
DR. M. ARUNA
India
ALIIHSAN SEKERTEKIN
Turkey
Yassine KADMI
French
Tariqual Islam Sajeeb
Dhaka
Jiban Shrestha
Nepal
Fredrick OJIJA
Tanzanian
Dr. Rasha Ali Eldeeb
Egypt
DR. PATRICK D. CERNA
Philippines
Dr. Amzad Basha Kolar
India
Prof. Suhail Hussein Al-Fatlawi
Iraq
ZiedIBN El Hadj
Tunisia
Zafer Omer Ozdemir
Turkey
Uthumporn Utra
Malasiya
Tarek Habeeb Ramadan Ahmed
Egypt
Syed Abdul Rehman Khan
China
Sri. P.C. Puri
India
Dr. Mohd Ramzi Bin Mohd Hussain
Malasiya
Peter Changilwa Kigwilu
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Lachachi Abdelheq
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Ibrahim Khider Ibrahim Osman
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Firuza M. Tursunkhodjaeva
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Faraz Ahmed Farooqi
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Elsadig Gasoom FadelAlla Elbashir
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DR. Yogesh Dnyandeo Narkhede
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Dr. Ashok Kumar
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Arshad Mehmood
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Anania B. Aquino
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Ahmed I.S. Ahmed
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Mexico
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India
Diones Krinski
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Aberham Kebedom Darge
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Atiya Firdous
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Prof. Marco Nemesio E.Montano
Phillippines
Prof. Zora Singh
AUSTRALIA
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Egypt
Moataz Mostafa El-Nahas Ali El-sherbini
Egypt
Prof. M. Abdul Mottaleb
Maryville
Dr. Ali Seidi
Iran