Analysis of Complex Customer Networks: A Real-World Banking Example

Complex customer networks are an important tool for better understanding of customer behaviour and customers’ mutual interdependence. Hence, they have proven to be insightful in numerous customer-centric domains such as recommender systems and customer relationship management. Although previous research in this field is predominantly associated with telecommunication and e-commerce sectors, similar construction principles could be transferred to the financial sector, e.g. a customer network could be created based on transaction history or domain-specific relations such as loan debtor–guarantor or loan debtor–co-debtor. However, this research path is still underdeveloped. In this paper, we analyse real-world complex customer networks of a Croatian bank. Numerous graph metrics were calculated, and the obtained results prove that these networks are non-random. We also show that these networks are scale-free and exhibit the small-world effect based on graph properties like the node degree distribution and the average shortest path.