Modeling of risk losses using size-biased data

Abstract

In this paper we present a method for drawing inferences about the process of financial losses that are associated with the operations of a business. For example, for a bank such losses may be related to erroneous transactions, human error, fraud, lawsuits, or power outages. Information about the frequency and magnitude of losses is obtained through the search of a number of sources, such as printed, computerized, or Internet-based publications related to insurance and finance. The data consists of losses that were discovered in the search. We assume that the probability of a loss appearing in the body of sources and also being discovered increases with the magnitude of the loss. Our approach simultaneously models the process of losses and the process of populating the database. The approach is illustrated using data related to operational risk losses that are of special interest to the banking industry.

An ontology modelling perspective on business reporting

“…probabilistic analysis of reporting data-statistical analysis of risks using event, cause and area taxonomies (for a general introduction, see [14], and for an example in the field of loss analysis, see [18]). …”

“…In the first place, these mechanisms will allow automated checkings of reporting data consistency and compliance in an elegant way. Second, inferencing is important in checking reporting data against advanced mathematical risk models (like in [18]) or qualitative assessments, and against business rules or regulations in general. Finally, inferencing can help to overcome a disadvantage of the linkbase representation of taxonomies as explained in the next paragraph.…”