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The claims are often modeled as probability of claim and claim amounts. The probability of claims are often modeled either with logit regression or with survival models. The claim amount (given that the claim was filed) is often modeled as a simple regression.

So, if I were you (and I was some time ago), I'd start with a simple regression on relevant variables. I modeled loss given defaults (LGD) on mortgages, and for me the relevant parameters were things like Zip code, house value (indexed), loan outstanding, etc.

Things to be careful about is restrictions such as nonnegativity of the claim and max amount. So, LGD is usually modeled as a percentage of the outstanding balance on a loan, in your case it's the max amount. LGD can go higher than 100%, but not by too much. It should not go below 0, but in the data there are always negative amounts in observations. There are many way when dealing with restrictions. Start with the simplest one: floors and ceilings, i.e. run unrestricted regression, then simply put floor and ceiling to the output.

The key is to start with the simplest models when you're a beginner in this area. You'll progressivly make more sophisticated models as time goes by, and you learn more about the domain.

The claims are often modeled as probability of claim and claim amounts. The probability of claims are often modeled either with logit regression or with survival models. The claim amount (given that the claim was filed) is often modeled as a simple regression.

So, if I were you (and I was some time ago), I'd start with a simple regression on relevant variables. I modeled loss given defaults on mortgages, and for me the relevant parameters were things like Zip code, house value (indexed), loan outstanding, etc.

The claims are often modeled as probability of claim and claim amounts. The probability of claims are often modeled either with logit regression or with survival models. The claim amount (given that the claim was filed) is often modeled as a simple regression.

So, if I were you (and I was some time ago), I'd start with a simple regression on relevant variables. I modeled loss given defaults (LGD) on mortgages, and for me the relevant parameters were things like Zip code, house value (indexed), loan outstanding, etc.

Things to be careful about is restrictions such as nonnegativity of the claim and max amount. So, LGD is usually modeled as a percentage of the outstanding balance on a loan, in your case it's the max amount. LGD can go higher than 100%, but not by too much. It should not go below 0, but in the data there are always negative amounts in observations. There are many way when dealing with restrictions. Start with the simplest one: floors and ceilings, i.e. run unrestricted regression, then simply put floor and ceiling to the output.

The key is to start with the simplest models when you're a beginner in this area. You'll progressivly make more sophisticated models as time goes by, and you learn more about the domain.

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source | link

The claims are often modeled as probability of claim and claim amounts. The probability of claims are often modeled either with logit regression or with survival models. The claim amount (given that the claim was filed) is often modeled as a simple regression.

So, if I were you (and I was some time ago), I'd start with a simple regression on relevant variables. I modeled loss given defaults on mortgages, and for me the relevant parameters were things like Zip code, house value (indexed), loan outstanding, etc.