Data Mining and Statistical Analysis of Completions in an Unconventional Play: The Canadian Montney Formation

Purpose The purpose of this paper is to use statistical techniques to mine and analyze suspicious transactions. With the increase in money laundering activities across various sectors in some of the world’s leading democracies, the ability to detect such transactions is gaining grounds with more urgency. Regulators and practitioners have been calling for an approach that can mine the large volume of unstructured data form suspicious money laundering transactions to inform public policies. Design/methodology/approach By deducing from the results of empirical studies in the field of money laundering detection, this paper presented an overview of data mining technology for detecting suspicious transactions. Findings After chronicling the data mining process, the paper delves into an analysis of the statistical approaches that can be used to differentiate between legitimate and suspicious money laundering transactions. The different stages of the data mining process are carefully explained in relation to their application to anti-money laundering compliance. The results indicate that statistical data mining methodology is a very efficient and useful technique to detect suspicious transactions. Practical implications The paper is of relevance to regulators and the financial service sector. A discussion of how data can be mined to facilitate statistical analysis can be used to inform regulatory policies on the detection and prevention of money laundering activities in the financial service sector. Originality/value The paper discuss approaches that illustrate how analysts can use statistical techniques to analyze data for suspicious money laundering transactions

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Erratum: Emerging Topics and Challenges for Statistical Analysis and Data Mining

Statistical Analysis and Data Mining The ASA Data Science Journal ◽

10.1002/sam.10121 ◽

2011 ◽

Vol 4 (3) ◽

pp. 353-353

Author(s):

Goodman Arnold

Keyword(s):

Data Mining ◽

Statistical Analysis ◽

Emerging Topics

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Deploying data mining techniques to gain deeper insight into Nigerian customers' financial activities

Nigerian Journal of Technology ◽

10.4314/njt.v39i2.26 ◽

2020 ◽

Vol 39 (2) ◽

pp. 553-561

Author(s):

A.M. Nwohiri ◽

F.T. Sonubi

Keyword(s):

Data Mining ◽

Information Processing ◽

Statistical Analysis ◽

Anomaly Detection ◽

Report Generation ◽

Processing Methods ◽

Data Mining Techniques ◽

Digital World ◽

Data Tables ◽

Insight Into

Presently, Nigerian banks issue account statements in a tabular flat form. These statements mainly show basic logs of credit and debit transactions. They do not offer a deeper insight into the pure nature of transactions. Moreover, they lack rich mine-able data, and rather contain basic data tables that do not provide enough insights into customers' monthly/weekly/yearly expenses and earnings. In today’s fast-paced digital world, where information processing methods are rapidly changing, customers need not just a basic table of transactions but deeper analysis and detail report of their finances. This paper aims at identifying and addressing these problems by deploying data mining techniques and practices in building an application that helps customers gain a deeper insight and understanding of their spending and earnings over a particular period. Some of the techniques used are classification, statistical analysis, visualization, report generation and summarization. Keywords: Data mining, API, Anomaly Detection, GTBank, CBN, Bank statements, Nigeria

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Using Selected Data-Mining Methods in the Analysis of Data Concerning the Attitudes of Students towards the Issue of Vaccination

Studies in Logic, Grammar and Rhetoric ◽

10.2478/slgr-2021-0031 ◽

2021 ◽

Vol 66 (3) ◽

pp. 549-559

Author(s):

Marcin Milewski ◽

Karolina Milewska ◽

Anna Justyna Milewska

Keyword(s):

Data Mining ◽

Statistical Analysis ◽

Severe Disease ◽

Modern Medicine ◽

The Other ◽

Main Method ◽

Preventive Vaccination ◽

Medical Faculties ◽

Level Of Knowledge ◽

Mining Methods

Abstract Preventive vaccination is one of the greatest successes of modern medicine. The SARS-CoV-2 epidemic, during which vaccination is the main method of prevention against death and severe disease, gave rise to a resurgence of anti-vaccine (anti-vax) movements. The aim of this study was to analyse the attitudes of students towards vaccination and the COVID-19 pandemic. The statistical analysis was performed with the use of the following data-mining methods: correspondence analysis and basket analysis. The obtained results show that students of medicine are characterized with the highest level of knowledge. Students of other medical faculties, on the other hand, have a significantly less uniform views, as do students of non-medical faculties.

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