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You are viewing ARCHIVED CONTENT released online between 1 April 2010 and 24 August 2018 or content that has been selectively archived and is no longer active. Content in this archive is NOT UPDATED, and links may not function.Extract from an article by Rasha Abdel Jalil
According to many financial institutions, big data analytics are becoming essential to tracking money laundering activities. Here we illustrate a few practical ideas to leverage big data and improve data quality:
Enhanced Knowledge Discovery in Databases (KDD): As long as the input data is poor, finding knowledge in data is almost impossible. Today, with the transformative impact of data, the value of obtained knowledge can be improved by enhancing the KDD process and applying a linked database approach, though this is not enough to achieve the target. FIs should adopt a cascading systems technique by adding another layer of systems which has the capability to interconnect with different databases and extract needed fields which will increase the accuracy of finding patterns of data.
Data deduplication: Be efficient and avoid duplication of data storage. Eliminating the redundant data has its value on the output of the earlier mentioned practices. Data deduplication has a vital role not only in reducing storage cost but also in increasing performance especially for real-time applications that receive a high amount of data and require regular data archives.
Data duplication detection can also help in uncovering some suspicious activities like multiple account opening for the same entity for money laundering or other fraudulent activities.
Read the complete article at How data analytics and visualisation revolutionised AML