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 |