A novel approach for cross-selling insurance products using positive unlabelled learning

Sidorowicz, Tomas, Peres, Pedro and Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478 2022. A novel approach for cross-selling insurance products using positive unlabelled learning. Presented at: 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 18 - 23 July 2022. 2022 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers, pp. 1-8. 10.1109/IJCNN55064.2022.9892762

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Abstract

Successful cross-selling of products is a key goal of companies operating within the insurance industry. Choosing the right customer to approach for cross-purchase opportunities has a direct effect on both decreasing customer churn rate and increasing revenue. Unlike sales data of general products, insurance sales data typically contains only a few products (e.g., private medical insurance, life insurance, etc), it is highly imbalanced with a vast majority of customers with no cross-purchasing information, highly noisy due to varying purchase behaviour between different customers, and has no ground truth for knowing if the majority customers are truly non-cross-sell customers or they are missed opportunities. These data challenges render the building of machine learning models for accurately identifying potential cross-sell customers extremely difficult. This paper proposes a novel approach to solve this challenging problem of cross-sell customer identification using Positive Unlabelled (PU) learning in conjunction with advanced feature engineering on customer demographic data and unstructured customer question-response texts through topic modelling. We implement a bagging approach to iteratively learn the positive samples (the confirmed cross-sells) alongside random sub-samples of the unlabelled set. The proposed approach is extensively evaluated on real insurance data that has been newly collected from a leading insurance company for this study. Evaluation results demonstrate that our approach can successfully identify new potential opportunities for likely cross-sell customers.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics Research Institutes & Centres > Data Innovation Research Institute (DIURI)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	positive unlabelled learning, cross-sell, bagging classification, topic modelling, text similarity
Publisher:	Institute of Electrical and Electronics Engineers
ISBN:	9781665495264
Date of First Compliant Deposit:	10 June 2022
Date of Acceptance:	26 April 2022
Last Modified:	28 Jun 2025 01:30
URI:	https://orca.cardiff.ac.uk/id/eprint/149953

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