For excess of loss reinsurance with retention , the direct insurer caps its payment at and the reinsurer pays the excess. Thus
Let and . These are independent Bernoulli random variables, independent of . Since , its conditional law given is a binomial distribution. Conditioning on therefore gives the Bernoulli thinning identity
Here the probability generating function may be evaluated on , and the identity extends wherever the transforms converge.
The positive reinsurer marks have the conditional law . Hence
The independent-mark representation is justified as follows. Conditional on any fixed count and pattern of exceedance indicators, the positive excesses are independent with this same conditional law; that law depends neither on the number of exceedances nor on their locations. Averaging over the indicator pattern thus leaves the positive mark sequence independent of . We may extend it by additional independent copies if needed, giving with the stated independence.
Now write . For exponential distribution claim sizes of mean ,
Thus is exponential with the original mean , by memorylessness of the exponential distribution. The original zero-based geometric distribution count has . Consequently
This is the geometric count under Bernoulli thinning, with
Finally use the Laplace transform of a nonnegative random variable, keeping the mass at zero. For ,
The last expression is the transform of a mixture distribution: mass at zero and, with probability , an exponential distribution of rate . Uniqueness of the Laplace transform identifies this zero-based geometric sum of exponential variables:
Equivalently, and its positive density is . Therefore the requested exceedance probability is
The prefactor is essential: the aggregate is zero whenever no claim crosses the retention.