A Hybrid Approach for Detecting Suspicious Accounts in Money Laundering Using Data Mining Techniques

Автор: Ch.Suresh, K.Thammi Reddy, N. Sweta

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 5 Vol. 8, 2016 года.

Бесплатный доступ

Money laundering is a criminal activity to disguise black money as white money. It is a process by which illegal funds and assets are converted into legitimate funds and assets. Money Laundering occurs in three stages: Placement, Layering, and Integration. It leads to various criminal activities like Political corruption, smuggling, financial frauds, etc. In India there is no successful Anti Money laundering techniques which are available. The Reserve Bank of India (RBI), has issued guidelines to identify the suspicious transactions and send it to Financial Intelligence Unit (FIU). FIU verifies if the transaction is actually suspicious or not. This process is time consuming and not suitable to identify the illegal transactions that occurs in the system. To overcome this problem we propose an efficient Anti Money Laundering technique which can able to identify the traversal path of the Laundered money using Hash based Association approach and successful in identifying agent and integrator in the layering stage of Money Laundering by Graph Theoretic Approach.

Еще

Data mining, Anti Money Laundering, FIU, Hash Based Mining, Traversal Path

Короткий адрес: https://sciup.org/15012484

IDR: 15012484

Список литературы A Hybrid Approach for Detecting Suspicious Accounts in Money Laundering Using Data Mining Techniques

  • NhienAn Le Khac, SammerMarkos, M. O'Neill, A. Brabazon and M-TaharKechadi. An investigation into Data Mining approaches for Anti Money Laundering. In International conference on Computer Engineering & Applications 2009.
  • Nhien An Le Khac, M.Teharkechadi. Application of Data mining for Anti-money Detection: A case study. IEEE International conference on Data mining workshops 2010.
  • Nhien An Le Khac, SammerMarkos,M.Teharkechadi,. A data mining based solution for detecting suspicious money laundering cases in an investment bank. IEEE Computer society 2010.
  • Yang Qifeng, Feng Bin, Song Ping. Study on Anti Money Laundering Service System of Online Payment based on Union-Bank mode. IEEE Computer Society 2007.
  • J.Han and M. Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann publishers, 2nd Eds., Nov 2005.
  • R.Corywatkins, K.Michaelreynolds, Ron Demara. Tracking Dirty Proceeds: Exploring Data Mining Techniques as to Investigate Money Laundering. In police practice and research 2003.
  • PankajRichhariya,PrashantK.Singh,EnduDuneja. A Survey on financial fraud detection methodologies. In International Journal of commerce business and management 2012.
  • J.S.Park, M.S.Chen, and P.S.Yu. An effective hash-based algorithm for mining association rules. In Proc. 1995 ACM-SIGMOD Int.Conf.Management of Data (SIGMOD’95), pages 175-186, San Jose, CA, May 1995.
  • Liu Keyan and Yu Tingting,”An improved Support vector Network Model for Anti-Money Laundering, International conference on Management of e-commerce ande-Government.
  • SreekumarPulakkazhy and R.V.S.Balan,”Data Mining in Banking and its applications –A Review”, Journal of computer science 2013.G.
  • G.Krishna priya,Dr.M.Prabakaran”Money laundering analysis based on Time variant Behavioral transaction patterns using Data mining”Journal of Theoretical and Applied Information Technology 2014.
  • Xingrong Luo,”Suspicious transaction detection for Anti Money Laundering”, International Journal of Security and Its Applications 2014.
  • ch suresh,Prof.K.Thammi Reddy,”A Graph based approach to identify suspicious accounts in the layering stage of Money laundering”,Global Journal of computer science and Information Technology 2014.
  • Denys A.Flores, Olga Angelopoulou, Richard J. Self,” Design of a Monitor for Detecting Money Laundering and Terrorist Financing”, International Journal of Computer Networks and Applications 2014.
  • Anu and Dr. Rajan Vohra,” Identifying Suspicious Transactions in Financial Intelligence Service”, International Journal of Computer Science & Management Studies July 2014.
  • Angela Samantha Maitland Irwin and Kim-Kwang Raymond Choo,” Modelling of money laundering and terrorism financing typologies”,Journal of Money laundering control 2012.
  • Pamela Castellón González,,Juan D. Velásquez ,” Characterization and detection of taxpayers with false invoices using data mining techniques”,Expert Systems with Applications ,Elsevier 2013.
  • Rafal Drezeswski,Jan sepielak,Wojciech Filip Kowsiki,”System supporting Money Laundering detection”, Elsevier 2012.
  • Quratulain Rajput, Nida Sadaf Khan, Asma Larik, Sajjad Haider, “Ontology Based Expert-System for Suspicious Transactions Detection”, Canadian Center of Science and Education, Computer and Information Science; Vol. 7, No. 1, 2014.
  • Mahesh Kharote, V. P. Kshirsagar, “Data Mining Model for Money Laundering Detection in Financial Domain”, International Journal of Computer Applications (0975 – 8887), Volume 85 – No 16, 2014.
  • Harmeet Kaur Khanuja, Dattatraya S. Adane, “Forensic Analysis for Monitoring Database Transactions”, Springer, Computer and Information Science Volume 467, pp 201-210, 2014.
  • Pradnya Kanhere, H. K. Khanuja,” A Survey on Outlier Detection in Financial Transactions,International Journal of computer Applications, December2014.
Еще
Статья научная