There is quite a lot of literature on OpRisk modelling. My question focuses on a loss distribution approach (LDA).
Let's look at a basic model. A Poisson-distributed $N$ and loss sizes $X_i$ and from the data I model $$ L = \sum_{i=1}^N X_i. $$ Then the regulator wants me to add some forward looking scenarios. There is some literature on this e.g. A “Toy” Model for Operational Risk Quantification using Credibility Theory. They often use Bayes methods and the scenarios have to address the parameters of the distributions involved.
I thought about something easier: if my expert says: "I expect a loss of 10K to happen once every 3 years" then I could model this scenaro as a Poisson $N_1$ with intensity $1/3$ and loss severity with point mass 1 at 10 000: $$ L_1 = \sum_{i=1}^{N_1} 10 000 = 10 000 N_1. $$ I could add this to my loss variable $L$: $L+L_1$ and incorporate many scenarios and still keep my model very tractable.
Is there something wrong or too simplifying in this approach? I have not seen it in papers. Is it just too easy for a paper? Thank you for any comments.