Improving lapse predictions (sigma 6/2017: LIFE IN-FORCE MANAGEMENT)

Data analytics – descriptive and predictive – can be used to improve retention of inforce business in several ways. Statistical models can be applied to investigate and better understand who lapses and why, using data systematically collected from different sources beyond traditional policyholder information. Based on the understanding of the underlying drivers, predictive models can then provide forecasts of consumer propensity to lapse in response to changes in different variables (eg, price changes). This can be used to appropriately allocate resources to reduce lapses.
To date, however, propensity-to-lapse models have been largely unsuccessful in predicting lapses. New data sources and machine learning algorithms are likely to improve their predictive power, but methods such as neural networks are like black boxes, designed to train and predict outcome without shedding light on the underlying decision path to lapse. A better understanding of why people lapse is needed and to this end, traditional statistical methods remain important analytical tools.
The life industry has been slow to implement advanced data mining and predictive modelling techniques. According to a survey, capturing reliable data and implementing the right solutions to analyse and interpret the data remain key challenges for insurers.48 Insurers currently use data analytics mainly for marketing and client segmentation purposes. However, using it for additional purposes, including predicting lapse rates, is likely to increase as its utility becomes more apparent.
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