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Auditing Crypto Currency Transactions: Anomaly Detection in Bitcoin

Moore, Paris (2018) Auditing Crypto Currency Transactions: Anomaly Detection in Bitcoin. Undergraduate thesis, Dublin, National College of Ireland.

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Abstract

Both “big data” and “analytics” have become popular keywords in many organizations. The power data analytics has on harnessing the increasing volumes, velocity and complexity of data in a world of constant change and disruptive technologies has been recognized. Many companies are making significant investments to better understand the impact of these capabilities on their businesses. One area with significant potential is the transformation of the audit. This project explores ways in which analytics can change and shape the work of accountants.

Anomaly detection plays a pivotal role in data mining since most outlying points contain crucial information for further investigation. In the financial world which the Bitcoin network is a part of, anomaly detection can indicate fraud. Using data mining tools such as Regression, we simultaneously examine the relationship among variables whilst visually inspecting the data for possible outliers. By doing so, I have chosen the world’s leading cryptocurrency, Bitcoin. This project will conclude with an in-depth analysis on whether or not data analytics can shape how effectively, and secure accountants can audit transactions by implementing analytics tools into their daily protocols.

Item Type: Thesis (Undergraduate)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

H Social Sciences > HG Finance > Money > Currency
Divisions: School of Computing > Bachelor of Science (Honours) in Computing
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 08 Nov 2018 13:43
Last Modified: 08 Nov 2018 13:43
URI: http://trap.ncirl.ie/id/eprint/3489

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