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Customer Payment Trend Analysis Based on Clustering for Predicting the Financial Risk of Business Organizations

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Title: Customer Payment Trend Analysis Based on Clustering for Predicting the Financial Risk of Business Organizations
Description: 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.
Authors: Jose, Jeeva
Resource Type: eBook.
Subjects: Financial risk, Business forecasting
Categories: BUSINESS & ECONOMICS / Management, BUSINESS & ECONOMICS / Management Science, BUSINESS & ECONOMICS / Industrial Management, BUSINESS & ECONOMICS / Organizational Behavior, MATHEMATICS / Pre-Calculus, MATHEMATICS / Reference, MATHEMATICS / Essays
Database: eBook Collection (EBSCOhost)
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PubType: eBook
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  Data: Customer Payment Trend Analysis Based on Clustering for Predicting the Financial Risk of Business Organizations
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  Label: Description
  Group: Ab
  Data: 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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  Data: <searchLink fieldCode="DE" term="%22Financial+risk%22">Financial risk</searchLink><br /><searchLink fieldCode="DE" term="%22Business+forecasting%22">Business forecasting</searchLink>
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RecordInfo BibRecord:
  BibEntity:
    Classifications:
      – Code: 658.40355
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Financial risk
        Type: general
      – SubjectFull: Business forecasting
        Type: general
    Titles:
      – TitleFull: Customer Payment Trend Analysis Based on Clustering for Predicting the Financial Risk of Business Organizations
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Jose, Jeeva
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          Name:
            NameFull: Jose, Jeeva
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2017
            – D: 25
              M: 04
              Type: profile
              Y: 2018
          Identifiers:
            – Type: isbn-print
              Value: 9783960671046
            – Type: isbn-electronic
              Value: 9783960676041
          Titles:
            – TitleFull: Customer Payment Trend Analysis Based on Clustering for Predicting the Financial Risk of Business Organizations
              Type: main
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