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Using Behavioral Analytics to Predict Customer Invoice Payment.

Mohsen Bahrami1,2, Burcin Bozkaya2,3, Selim Balcisoy4

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Summary
This summary is machine-generated.

Late invoice payments are common, impacting business cash flow. This study predicts customer payment behavior using machine learning, achieving 97% accuracy to improve financial stability.

Keywords:
behavioral analyticsinvoice collectioninvoice to cashlogistic regressionmachine learningpredictive analytics

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Area of Science:

  • Business Analytics
  • Machine Learning Applications
  • Financial Management

Background:

  • Many businesses struggle with late customer invoice payments, affecting cash flow.
  • Nearly half of small-to-medium enterprise (SME) and business-to-business (B2B) invoices in the US and UK are paid late.

Purpose of the Study:

  • To understand customer payment behavior and develop predictive models for invoice payments.
  • To create a decision support system for predicting future payments and managing debt collection.

Main Methods:

  • Utilized a dataset of over 1.6 million customers, including invoice/payment history and company collection actions.
  • Applied supervised and unsupervised learning techniques, including a novel behavioral scoring model.
  • Tested logistic regression, achieving up to 97% accuracy in predicting payment behavior.

Main Results:

  • Logistic regression models accurately predicted customer invoice payment behavior.
  • The model achieved up to 97% accuracy, with or without customer pre-clustering.
  • A novel behavioral scoring model enhanced predictive capabilities.

Conclusions:

  • The developed predictive model can significantly aid decision-makers in financial planning and cash flow management.
  • Accurate payment prediction helps in proactive debt collection and reduces reliance on corporate credit lines.
  • Implementing this analytical approach enhances corporate financial stability.