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I am working for a logistics firm and there are approx. 750+ customers who avail our services. I am in the process of building and generating some insight for the business based on the payments made by these customers for the last 1 year. Some make payment on time whereas some are late & some are extremely late. Could you please advice which modeling technique or statistical approach would be best in this case. I can think of creating clusters based on the payment history and highlights & placing all defaulters in one cluster where our company can focus. PLease suggest.

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    $\begingroup$ "Generate insight" is far too broad; until you know what you're asking it's not useful to ask about tools. $\endgroup$ – Sean Owen Apr 10 '15 at 21:33
  • $\begingroup$ Hi Sean, Generating insight by here I mean- finding insights such as certain descriptive statistics- mean payment days, median payment days, default by how many days, creating a delay band i.e. 1-10 days, 11-20 days etc. region wise details, BU wise details. $\endgroup$ – Shivi Apr 11 '15 at 5:02
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Here are some things that come to mind:

1) ARMA/ARIMA models: These may show if there is any seasonal trends in their payment history.

2) Logistic Regression: Will allow you model the conditional probability of a missed payments based off of number of previous missed payments.

From here you need to determine what the threshold for risk is since that's a little more subjective.

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