scholarly journals Determining customer segmentation and behaviour models with database marketing and machine learning

Pressacademia ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 89-111
Author(s):  
Orkun Berk Koca
Keyword(s):  
Machine Learning ◽  
Customer Segmentation ◽  
Database Marketing ◽  
Behaviour Models

Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

2019 ◽  
pp. 161-194
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analysis ◽  
Customer Segmentation ◽  
Learning Context ◽  
Feature Vectors

Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.

scholarly journals CUSTOMER SEGMENTATION IN PRIVATE BANKING SECTOR USING MACHINE LEARNING TECHNIQUES

2013 ◽  
Vol 14 (5) ◽  
pp. 923-939 ◽  
Author(s):  
Ion Smeureanu ◽  
Gheorghe Ruxanda ◽  
Laura Maria Badea
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Foreign Exchange Market ◽  
Banking Sector ◽  
Customer Segmentation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Fraud Prediction ◽  
Private Banking

Machine learning techniques have proven good performance in classification matters of all kinds: medical diagnosis, character recognition, credit default and fraud prediction, and also foreign exchange market prognosis. Customer segmentation in private banking sector is an important step for profitable business development, enabling financial institutions to address their products and services to homogeneous classes of customers. This paper approaches two of the most popular machine learning techniques, Neural Networks and Support Vector Machines, and describes how each of these perform in a segmentation process.

scholarly journals Customer Segmentation Using Machine Learning

2021 ◽  
pp. 207-211
Author(s):  
Varad R Thalkar
Keyword(s):  
Machine Learning ◽  
Customer Segmentation ◽  
Business Decisions ◽  
Demand And Supply ◽  
Potential Customer ◽  
Customer Base ◽  
Customer Defection ◽  
Customer Segment

Customer Segmentation is the process of division of customer base into several groups called as customer segments such that each customer segment consists of customers who have similar characteristics. Segmentation is based on the similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.The customer segmentation has the importance as it includes, the ability to modify the programs of market so that it is suitable to each of the customer segment, support in business decisions; identification of products associated with each customer segment and to mange the demand and supply of that product; identifying and targeting the potential customer base, and predicting customer defection, providing directions in finding the solutions.

Illicit Bitcoin transactions: challenges in getting to the who, what, when and where

2018 ◽  
Vol 21 (3) ◽  
pp. 297-313 ◽  
Author(s):  
Angela S.M. Irwin ◽  
Adam B. Turner
Keyword(s):  
Machine Learning ◽  
Law Enforcement ◽  
Cyber Security ◽  
Optimal Solution ◽  
Law Enforcement Agencies ◽  
Content Type ◽  
Predictive Policing ◽  
External Data ◽  
Functional Software ◽  
Behaviour Models

Purpose The purpose of this paper is to highlight the intelligence and investigatory challenges experienced by law enforcement agencies in discovering the identity of illicit Bitcoin users and the transactions that they perform. This paper proposes solutions to assist law enforcement agencies in piecing together the disparate and complex technical, behavioural and criminological elements that make up cybercriminal offending. Design/methodology/approach A literature review was conducted to highlight the main law enforcement challenges and discussions and examine current discourse in the areas of anonymity and attribution. The paper also looked at other research and projects that aim to identify illicit transactions involving cryptocurrencies and the darknet. Findings An optimal solution would be one which has a predictive capability and a machine learning architecture which automatically collects and analyses data from the Bitcoin blockchain and other external data sources and applies search criteria matching, indexing and clustering to identify suspicious behaviours. The implementation of a machine learning architecture would help improve results over time and would be less manpower intensive. Cyber investigators would also receive intelligence in a format and language that they understand and it would allow for intelligence-led and predictive policing rather than reactive policing. The optimal solution would be one which allows for intelligence-led, predictive policing and enables and encourages information sharing between multiple stakeholders from the law enforcement, financial intelligence units, cyber security organisations and fintech industry. This would enable the creation of red flags and behaviour models and the provision of up-to-date intelligence on the threat landscape to form a viable intelligence product for law enforcement agencies so that they can more easily get to the who, what, when and where. Originality/value The development of a functional software architecture that, in theory, could be used to detected suspicious illicit transactions on the Bitcoin network.

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