LIPTUS: associating structured and unstructured information in a banking environment

ABSTRACT

Growing competition has made today's banks understand the value of knowing their customers better. In this paper, we describe a tool, LIPTUS, that associates the customer interactions (emails and transcribed phone calls) with customer and account profiles stored in an existing data warehouse. The associations discovered by LIPTUS enable analytics spanning the customer and account profiles on one hand and the meta-data associated or derived from the interaction (using text mining techniques) on the other. We illustrate the value derived from this consolidated analysis through specific customer intelligence applications. LIPTUS is today being extensively used in a large bank in India. A highlight of this paper is a discussion of the technical challenges encountered while building LIPTUS and deploying it on real-life customer data.

REFERENCES

• 1: Borthwick, A., Sterling, J., Agichtein, E., and Grishman, R. Exploiting diverse knowledge sources via maximum entropy in named entity recognition. In Workshop on Very Large Corpora (1998).
• 2: Chakaravarthy, V., Gupta, H., Roy, P., and Mohania, M. Efficiently linking text documents with relevant structured information. In VLDB (2006).
• 3: Chawla, N., Japkowicz, N., and Kotcz, A. Editorial: Special issue on learning from imbalanced data sets. In SIGKDD Explorations (2004).
• 4: Hao Chen , Jianying Hu , Richard W. Sproat, Integrating geometrical and linguistic analysis for email signature block parsing, ACM Transactions on Information Systems (TOIS), v.17 n.4, p.343-366, Oct. 1999 [doi>10.1145/326440.326442]
• 5: Cohen, W., and Sarawagi, S. Exploiting dictionaries in named entity extraction: Combining semi-markov extraction process and data integration methods. In SIGKDD (2004).
• 6: T. Götz , O. Suhre, Design and implementation of the UIMA common analysis system, IBM Systems Journal, v.43 n.3, p.476-489, July 2004
• 7: Yoshihiko Hamamoto , Shunji Uchimura , Shingo Tomita, A Bootstrap Technique for Nearest Neighbor Classifier Design, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.1, p.73-79, January 1997 [doi>10.1109/34.566814]
• 8: Hu, M., and Liu, B. Mining and summarizing customer reviews. In SIGKDD (2004).
• 9: IBM. Made in IBM Labs: IBM Helps HDFC Bank Extract Information Insight to Enhance Customer Care. http://www.ibm.com/press/us/en/pressrelease/20729.wss.
• 10: Joshi, S., Ramakrishnan, G., Balakrishnan, S., and Srinivasan, A. Aggregating contextual patterns for information extraction. In IJCAI 2007 Workshopon Text Mining and Link Analysis (2007).
• 11: Christopher D. Manning , Hinrich Schütze, Foundations of statistical natural language processing, MIT Press, Cambridge, MA, 1999
• 12: Mladenic, D., and Grobelnik, M. Feature selection for unbalanced class distribution and naive bayes. In ICML (1999).
• 13: Roy, P., Mohania, M., Bamba, B. and Raman, S. Associating relevant unstructured content with structured database query results. In ACM CIKM(2005).
• 14: Fabrizio Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys (CSUR), v.34 n.1, p.1-47, March 2002 [doi>10.1145/505282.505283]
• 15: Jordi Turmo , Alicia Ageno , Neus Català, Adaptive information extraction, ACM Computing Surveys (CSUR), v.38 n.2, p.4-es, 2006 [doi>10.1145/1132956.1132957]
• 16: Yang, Y., and Pedersen, J. A comparative study on feature selection in text categorization. In ICML (1997).
• 17: Yi, J., and Niblack, W. Sentiment mining in web-fountain. In ICDE (2005).

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems
          Subjects: Textual databases

General Terms:
Algorithms, Design, Experimentation

Keywords:
customer intelligence, customer support, information integration