11Disentangling frequencies and decompounding losses
Even though today’s data collection tools may allow for a high level of discrimination, it is still plausible that in many fields loss data comes in aggregate form.
For example, when recording losses due to mistyped transaction orders, the information about the person typing the order or the nature of the transmission error is not recorded. Or consider for example the losses due to fraud, where the total fraud events at some location or fraud events of some type are known, but they may have different causes. Or an insurance company may record all claims due to collisions but only keep the data about the car owner and not about the person(s) causing the accident.
It may be important for the ...
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