FOR3705
Assignment 1 Semester 1 2025
Detailed Solutions, References & Explanations
Unique number:
Due Date: 29 March 2025
Question 1
1.1 Analyse the relationship between data mining and data analysis in financial crime
investigation
Data mining and data analysis are closely interconnected techniques used in the investigation
of financial crimes. Data mining involves the automated process of identifying hidden patterns,
correlations, and anomalies within large datasets using algorithms, machine learning, and
artificial intelligence. It focuses on extracting potentially useful and previously unknown
information from vast quantities of structured and unstructured data. On the other hand, data
analysis interprets and evaluates this information to draw meaningful conclusions that can
support investigative decisions. In financial crime investigations, data mining identifies unusual
financial transactions, such as repeated large deposits or transfers to high-risk jurisdictions,
while data analysis evaluates the significance of these transactions in relation to known
fraudulent behaviours. The relationship between the two is cyclical and dynamic—data mining
Terms of use
By making
uncovers the data points of interest, and data analysis refines use of this document
and contextualises you to
them agree to:
build
Use this document as a guide for learning, comparison and reference purpose,
Terms of use
Not to duplicate, reproduce and/or misrepresent the contents of this document as your own work,
By making use of this document you agree to:
Use this document
Fully accept the consequences
solely as a guide forshould you plagiarise
learning, reference,or and
misuse this document.
comparison purposes,
Ensure originality of your own work, and fully accept the consequences should you plagiarise or misuse this document.
Comply with all relevant standards, guidelines, regulations, and legislation governing academic and written work.
Disclaimer
Great care has been taken in the preparation of this document; however, the contents are provided "as is" without any express or
implied representations or warranties. The author accepts no responsibility or liability for any actions taken based on the
information contained within this document. This document is intended solely for comparison, research, and reference purposes.
Reproduction, resale, or transmission of any part of this document, in any form or by any means, is strictly prohibited.
Assignment 1 Semester 1 2025
Detailed Solutions, References & Explanations
Unique number:
Due Date: 29 March 2025
Question 1
1.1 Analyse the relationship between data mining and data analysis in financial crime
investigation
Data mining and data analysis are closely interconnected techniques used in the investigation
of financial crimes. Data mining involves the automated process of identifying hidden patterns,
correlations, and anomalies within large datasets using algorithms, machine learning, and
artificial intelligence. It focuses on extracting potentially useful and previously unknown
information from vast quantities of structured and unstructured data. On the other hand, data
analysis interprets and evaluates this information to draw meaningful conclusions that can
support investigative decisions. In financial crime investigations, data mining identifies unusual
financial transactions, such as repeated large deposits or transfers to high-risk jurisdictions,
while data analysis evaluates the significance of these transactions in relation to known
fraudulent behaviours. The relationship between the two is cyclical and dynamic—data mining
Terms of use
By making
uncovers the data points of interest, and data analysis refines use of this document
and contextualises you to
them agree to:
build
Use this document as a guide for learning, comparison and reference purpose,
Terms of use
Not to duplicate, reproduce and/or misrepresent the contents of this document as your own work,
By making use of this document you agree to:
Use this document
Fully accept the consequences
solely as a guide forshould you plagiarise
learning, reference,or and
misuse this document.
comparison purposes,
Ensure originality of your own work, and fully accept the consequences should you plagiarise or misuse this document.
Comply with all relevant standards, guidelines, regulations, and legislation governing academic and written work.
Disclaimer
Great care has been taken in the preparation of this document; however, the contents are provided "as is" without any express or
implied representations or warranties. The author accepts no responsibility or liability for any actions taken based on the
information contained within this document. This document is intended solely for comparison, research, and reference purposes.
Reproduction, resale, or transmission of any part of this document, in any form or by any means, is strictly prohibited.
