Abstract
In an insurance company, manual underwriting is costly, time consuming, and complex. Simulating underwriters with AI is an absolutely time saving and cost fitting solution. As a result, a Hybrid Multiple Classifier System, combining three classifiers with rejection options: XGBoost, Random Forest, and SVM, was designed and applied on production. An optimal rejection criterion on classification, so-called Linear Discriminant Analysis Measurement (LDAM), is applied the first time in industry. This system is the first AI driven underwriting system in Canadian life insurance, and it helps Manulife expand digital capabilities, reorient customer experience focus and grow its business.
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He, C.L., Keirstead, D., Suen, C.Y. (2020). A Hybrid Multiple Classifier System Applied in Life Insurance Underwriting. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_15
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DOI: https://doi.org/10.1007/978-3-030-59830-3_15
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