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I am new in forecasting and I am studying a dataset from an insurance company that contains the volume on a monthly basis of new policies, renewals & cancellations. New policies of a given month are renewed in intervals (3 months, 6 months, 12 months) but could be canceled as well at any time. For instance, new policies of January with 3-months duration are renewed after 3 months in April.

I would like some help in what direction to study in order to build a forecast model that will predict the amount of renewals (R) and cancellations (C) of a given month based on the input of new policies (N) of previous months.

Thank you

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I assume Renewals/Cancellations are your expected output, so it can be considered as a binary classification.

From where I stand, all you mentioned (policies) in your first paragraph is meaningless because I cannot see how I can use them to build any model. Rather, you better study what the clients who used to renew/cancel respectively look like. And that is the mighty "feature"! For example, you might take into consideration their gender, age, ethnicity, and annual income, etc. That would be more helpful than thinking only about your policy.

Or, you can also try a probabilistic solution: think about the probability of the occurrence of cancellation of your company in the past, and the probability of the occurrence of cancellation of other companies given that they execute the same policy. What can you predict?

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  • $\begingroup$ Sorry if I was not clear enough. I was thinking more of a time-related model like the following Rt=Nt+Tt for renewals and the respective one for cancellations. This way I am going to combine the new policies of previous times that got renewed plus time evolution. As I said I am new to this and I am trying to define the best approach. These two propositions of yours are quite interesting though ! $\endgroup$
    – Minos
    Apr 21 at 5:16

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