In contrast to traditional predictive data mining techniques, the research domain of social network analysis focuses on the interrelationship between customers to obtain better insights in the propagation of e.g. marketing campaigns, customer churn and retention, and fraudulent behavior. Customers directly and indirectly influence one other. Lots of valuable data and information is hidden in the underlying network structure connecting customers to each other. The research team of prof. Baesens is specialized in analyzing networked data for customer churn and fraud detection.
Customer churn prediction
Apart from new customer attraction, one of a company’s key challenges is to concentrate on customer retention, i.e. avoiding customers to go to the competition. Companies offer a wide range of marketing incentives as a reaction to churn, targeting not always the most promising customers. Although the team already extensively researched traditional data mining techniques for churn prediction with success, social network analysis looks at the problem from a new angle: a customer’s decision to churn is often not an individual choice, but is triggered by social influence of other (already churned) customers. Interrelational data plays an important role to extract critical insights in the characteristics of the propagation of churn. Moreover, the state-of-the-art algorithms of the research team of prof. Baesens do not only aim to predict the most likely churners, but particularly focus on the customers for who a marketing incentive has the largest retention effect.
Fraud is not often something an individual would commit by himself, but it is a well-considered and carefully organized crime set up by groups of people loosely connected to each other. The use of networked data in fraud detection becomes increasingly important to uncover fraudulent patterns and to detect in real-time when certain processes show some characteristics of irregular activities. Although all analyses focus in the first place on fraud detection, the emphasis of our research shifts towards fraud prevention, i.e. detecting fraud before it is even committed. As fraud is a time-evolving phenomenon, it is a challenge for social network algorithms to keep ahead of new types of fraud and to adapt to changing environment and surrounding effects.
- Van Vlasselaer, V., Akoglu, L., Eliassi-Rad, T., Snoeck, M., Baesens, B. (2015). Guilt-by-constellation: fraud detection by suspicious clique memberships. Proceedings of 48 Annual Hawaii International Conference on System Sciences: Vol. accepted. HICSS-48. Kauai (Hawaii), 5-8 January 2015.
- Van Vlasselaer, V., Akoglu, L., Eliassi-Rad, T., Snoeck, M., Baesens, B. (2014). Finding cliques in large fraudulent networks: theory and insights. Conference of the International Federation of Operational Research Societies (IFORS 2014). Barcelona (Spain), 13-18 July 2014.
- Van Vlasselaer, V., Akoglu, L., Eliassi-Rad, T., Snoeck, M., Baesens, B. (2014). Gotch’all! Advanced network analysis for detecting groups of fraud. PAW (Predictive Analytics World). London (UK), 29-30 October 2014.
- Van Vlasselaer, V., Van Dromme, D., Baesens, B. (2013). Social network analysis for detecting spider constructions in social security fraud: new insights and challenges: vol. accepted. European Conference on Operational Research. Rome (Italy), 1-4 July 2013.
- Van Vlasselaer, V., Meskens, J., Van Dromme, D., Baesens, B. (2013). Using social network knowledge for detecting spider constructions in social security fraud. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. ASONAM. Niagara Falls (Canada), 25-28 August 2013 (pp. 813-820). 445 Hoes Lane, PO Box 1331, Piscataway, NJ 08855-1331, USA: IEEE Computer Society.
- Verbraken, T., Goethals, F., Verbeke, W., Baesens, B. (2012). Using social network classifiers for predicting e-commerce adoption. E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life: Vol. 108. The Tenth Workshop on E-Business (WEB2011). Shanghai (China), 4 December 2011 (pp. 9-21).
- Verbeke, W., Dejaeger, K., Verbraken, T., Martens, D., Baesens, B. (2011). Mining social networks for customer churn prediction: vol. accepted. Interdisciplinary Workshop on Information and Decision in Social Networks. Cambridge (US), 31 May – 1 June 2011.
- Van Vlasselaer, V. (2012). Mining Data on Twitter. Master Thesis.