Suchergebnisse

What triggers the information exchange among the regional offices in Brussels? To answer this question, we develop a framework based on network theory and present the first large-N quantitative study on European Union-level inter-regional networks. Our results show that information exchanges take place predominantly among regions from the same member state. Furthermore, the likelihood of regular information exchanges between two regional offices depends on the resources invested in regional representation, the joint involvement in inter-regional associations, and geographical proximity. In contrast to our expectations, our findings refute the hypotheses that more regional autonomy and more experience in Brussels increase the propensity toward cross-border inter-regional networking, a practice that might potentially contribute to the fragmentation of member state representation.

The interaction between organised interests and policy makers is an important ingredient of contemporary political systems. In earlier work, interest group scholars have distinguished groups that enjoy access to consultation arrangements from those that are bound to stand on the sideline. Frequently, these insiders are considered to be equally connected to public authorities. Yet their degree of 'insiderness' differs significantly. By unpacking the set of organised interests that have gained access, this article distinguishes core insiders from groups that occupy a more peripheral position in an interest intermediation system. Empirically, we demonstrate and explain varying degrees of insiderness in the community of insider groups in Belgium, using the extensiveness of representation in advisory bodies as a proxy for access. Our findings show that, although nowadays a diverse set of organised interests gets involved in policy-making processes, the inner circle is dominated by traditional economic interests.

AbstractInsurance fraud occurs when policyholders file claims that are exaggerated or based on intentional damages. This contribution develops a fraud detection strategy by extracting insightful information from the social network of a claim. First, we construct a network by linking claims with all their involved parties, including the policyholders, brokers, experts, and garages. Next, we establish fraud as a social phenomenon in the network and use the BiRank algorithm with a fraud‐specific query vector to compute a fraud score for each claim. From the network, we extract features related to the fraud scores as well as the claims' neighborhood structure. Finally, we combine these network features with the claim‐specific features and build a supervised model with fraud in motor insurance as the target variable. Although we build a model for only motor insurance, the network includes claims from all available lines of business. Our results show that models with features derived from the network perform well when detecting fraud and even outperform the models using only the classical claim‐specific features. Combining network and claim‐specific features further improves the performance of supervised learning models to detect fraud. The resulting model flags highly suspicions claims that need to be further investigated. Our approach provides a guided and intelligent selection of claims and contributes to a more effective fraud investigation process.