A novel dynamic approach to identifying suspicious customers in money transactions

Abdul Khalique Shaikh*, Amril Nazir

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Money laundering activity causes a negative impact on the development of the national economy. Anti-money laundering (AML) solutions within financial institutions facilitate to control it in a suitable way. However, one of the fundamental challenges in AML solution is to identify real suspicious transactions. To identify these types of transactions, existing research uses pre-defined rules and statistical approaches that help to detect the suspicious transactions. However, due to the fixed and predetermined rules, it is highly probable that a normal customer can be identified as suspicious customers. To overcome the above limitations, a novel dynamic approach to identifying suspicious customers in money transactions is proposed that is based on dynamic analysis of customer profile features to identify suspicious transactions. The experiment has been executed with real bank customers and their transactions data and the results of the experiment provide promising outcomes in terms of accuracy.

Original languageEnglish
Pages (from-to)143-158
Number of pages16
JournalInternational Journal of Business Intelligence and Data Mining
Volume17
Issue number2
DOIs
Publication statusPublished - Jan 1 2020

Keywords

  • AML
  • Anti-money laundering
  • Data analysis
  • Dynamic AML analysis
  • Money transaction
  • Suspicious transactions

ASJC Scopus subject areas

  • Management Information Systems
  • Statistics, Probability and Uncertainty
  • Information Systems and Management

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