Emerging topics and challenges for statistical analysis and data mining

2011 ◽  
Vol 4 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Arnold Goodman
Keyword(s):  
Data Mining ◽  
Statistical Analysis ◽  
Emerging Topics

scholarly journals Data mining for statistical analysis of money laundering transactions

2019 ◽  
Vol 22 (4) ◽  
pp. 753-763
Author(s):  
Mark Eshwar Lokanan
Keyword(s):  
Data Mining ◽  
Statistical Analysis ◽  
Money Laundering ◽  
Service Sector ◽  
Empirical Studies ◽  
Financial Service ◽  
Statistical Techniques ◽  
Content Type ◽  
Regulatory Policies ◽  
Statistical Data Mining

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

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

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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