Customer Payment Trend Analysis based on Clustering for Predicting the Financial Risk of Business Organizations (PDF)
(Sprache: Englisch)
With the opening of the Indian economy, many multinational corporations are shifting their manufacturing base to India. This includes setting up green field projects or acquiring established business firms of India. The region of this business unit is...
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With the opening of the Indian economy, many multinational corporations are shifting their manufacturing base to India. This includes setting up green field projects or acquiring established business firms of India. The region of this business unit is expanding globally. The variety and size of the customer base is expanding and the business risk related to bad debts is increasing. Close monitoring and analysis of payment trends helps to predict customer behavior and predict the chances of customer financial strength.
The present manufacturing companies generate and store tremendous amount of data. The amount of data is so huge that manual analysis of the data is difficult. This creates a great demand for data mining to extract useful information buried within these data sets. One of the major concerns that affect companies' investments and profitability is bad debts; this can be reduced by identifying past customer behavior and reaching the suitable payment terms. The Clustering and Prediction module was implemented in WEKA - a free open source software written in Java. This study model can be extended to the development of a general purpose software package to predict payment trends of customers in any organisation.
The present manufacturing companies generate and store tremendous amount of data. The amount of data is so huge that manual analysis of the data is difficult. This creates a great demand for data mining to extract useful information buried within these data sets. One of the major concerns that affect companies' investments and profitability is bad debts; this can be reduced by identifying past customer behavior and reaching the suitable payment terms. The Clustering and Prediction module was implemented in WEKA - a free open source software written in Java. This study model can be extended to the development of a general purpose software package to predict payment trends of customers in any organisation.
Autoren-Porträt von Jeeva Jose
Prof. Jeeva Jose was awarded PhD in Computer Science from Mahatma Gandhi University, Kerala, India and is a faculty member at BPC College, Kerala. Her passion is teaching and areas of interests include World Wide Web, Data Mining and Cyber laws. She has been in higher education since year 2000 years and has completed three research projects funded by UGC and KSCSTE. She has authored and published five books. She has published more than twenty research papers in various refereed journals and conference proceedings. She has edited three books and has given many invited talks in various conferences. She is a recipient of ACM-W Scholarship provided by Association for Computing Machinery, New York.
Bibliographische Angaben
- Autor: Jeeva Jose
- 2016, 1. Auflage, 71 Seiten, Englisch
- Verlag: Diplomica Verlag
- ISBN-10: 3960676042
- ISBN-13: 9783960676041
- Erscheinungsdatum: 22.11.2016
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- Größe: 7.17 MB
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Englisch
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