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dc.contributor.advisor Perera I
dc.contributor.author Maduranga WAH
dc.date.accessioned 2022
dc.date.available 2022
dc.date.issued 2022
dc.identifier.citation Maduranga, W.A.H. (2022). Micro data model architecture for AML scoring rule engines [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21546
dc.identifier.uri http://dl.lib.uom.lk/handle/123/21546
dc.description.abstract Online and mobile banking have become a primary service of today’s banking and financial sector. Clients could do their primary transactional jobs without physically appearing on the bank. This facility is 24x7 available. So, detection of money laundering activities based on transactional data analysis is a key challengeable area in today’s banking and financial sector. Businesses are trying to prevent money laundering activities by applying rule-based techniques to the real time operational transactions which could not completely cure the problem because higher constraints on the operational transaction could inconvenience the legal customer base and lose the customer satisfaction over the time. So, the near-real time and traditional data warehousing approaches with post detection techniques becomes the most common approach to detect money laundering activities in today’s banking and financial context. Traditional data warehousing approaches loaded data from operational or transactional systems on a weekly or nightly basis. Near real-time and real-time data warehouse approaches use real-time ETL tools to load data into the data warehouse in predefined shorter time intervals which preserve a gap with real-time transactional data. In addition to that, running anomaly detection engines (rule based or machine learning models) on top of those massive amounts of data (either OLTP databases or warehouse database) will take another considerable time due to higher velocity of data. So, identifying money launderers by analyzing post detection techniques causes higher risk to the financial system because the money launderer may leave the financial system before the money launderer catches. This report introduce a novel data modelling architecture named “Micro Data Model Architecture” and an associated supporting tool named “Micro Temporal Database Generator” for “scoring rule engines” to detect financial fraudulent activities earlier by removing the burden on operational data sources. en_US
dc.language.iso en en_US
dc.subject DATA MODELING en_US
dc.subject MICRO DATA MODEL ARCHITECTURE en_US
dc.subject AML SCORING en_US
dc.subject COMPUTER SCIENCE -Dissertation en_US
dc.subject INFORMATION TECHNOLOGY -Dissertation en_US
dc.title Micro data model architecture for AML scoring rule engines en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.degree MSc In Computer Science and Engineering en_US
dc.identifier.department Department of Computer Science and Engineering en_US
dc.date.accept 2022
dc.identifier.accno TH4965 en_US


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