Seminar: Social networks with applications in finance and industry

Overview

This lecture discussed how to leverage social networks effectively and efficiently for predictive modelling. Three practical applications were chosen: churn prediction in the telecommunications industry, credit scoring, and insurance fraud detection.

In the first two situations, networks were built using call detail records (CDR), which gave an accurate depiction of people's activity and so became a valuable source of data for researchers in fields such as physics, sociology, epidemiology, transportation, and networking.

The performance of network learning techniques for churn prediction was compared to that of normal binary classifiers (such as logistic regression and random forests) with network characteristics extracted, which was a more traditional approach. According to the findings, churn influence does not extend widely in the social network, and churn status inside a customer's ego-net is strongly predictive of churn.

The conference highlighted the added value of integrating cell phone data, or CDR, in credit risk modelling in the second application, credit scoring. The results showed that variables reflecting calling behaviour were the best predictive in terms of statistical and economic model performance. These findings also revealed significant regulatory, privacy, and ethical consequences.

Finally, the purpose was to discover groups of collaborating fraudsters by connecting claims and the parties involved in a massive social network to detect fraudulent insurance claims. As a result, rather than just the traditional components of the claim, the policyholder, and the policy, the social structures of fraudsters have also been established in insurance fraud detection approaches and models.