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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.

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Prof. Jeeva Jose erned a PhD in Computer Science from Mahatma Gandhi University, Kerala, India and is a faculty member at BPC College, Kerala. Her passion is teaching and her areas of interest include World Wide Web, Data Mining and Cyber laws. She has been in higher education for the last 16 years and has completed three research projects funded by UGC and KSCSTE so far.
Hitherto, the author has authored and published three books. She has also published more than twenty research papers in various refereed journals and conference proceedings. She has edited three books and has held many speeches on various conferences. She is a recipient of an ACM-W Scholarship provided by the Association for Computing Machinery, New York.

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