Applying imputation and statistical learning to predict gamma-glutamyl transferase in underwriting data
type: CreativeWork
description: Insurance underwriting can be time-consuming and costly for both insurers and customers. However, the insight gained is of critical importance in addressing the information asymmetry between insurers and customers in terms of establishing a customer's risk profile. Consequently, any test that assists in providing a risk assessment is critical in allowing insurance companies to manage risk and price their products appropriately. Gamma-glutamyl Transferase (GGT) is an enzyme which has been used by insurers in underwriting medical tests as an indicator of potential adverse outcomes. However, due to complexities such as differing underwriting strategies, data collection and data storage issues, not every customer on an insurer's books will have a GGT value or even a complete data profile. This research investigates if statistical techniques such as imputation and supervised learning can be used in conjunction with available medical, demographic, underwriting and policy data to accurately predict GGT values. A combination of multivariate imputation by chained equations (MICE) and extremegradient boosted trees (XGBoost) offers a 31% improvement in accuracy compared to a naïve prediction. However, there does appear to be a limit to the performance achieved from all implemented techniques with the analysed dataset, with various model combinations yielding root mean squared error (RMSE) values within a narrow range. In addition, when comparing the predictions from a separate, unlabelled dataset to actual data, it appears as though predictions from the models cannot be reliably deemed to be from the same distribution. This indicates that further research is required before insurers can reliably switch out blood-work based GGT results for those from a supervised learning model. Keywords: insurance, underwriting, gamma-glutamyl transferase, imputation, supervised learning
abstract: Insurance underwriting can be time-consuming and costly for both insurers and customers. However, the insight gained is of critical importance in addressing the information asymmetry between insurers and customers in terms of establishing a customer's risk profile. Consequently, any test that assists in providing a risk assessment is critical in allowing insurance companies to manage risk and price their products appropriately. Gamma-glutamyl Transferase (GGT) is an enzyme which has been used by insurers in underwriting medical tests as an indicator of potential adverse outcomes. However, due to complexities such as differing underwriting strategies, data collection and data storage issues, not every customer on an insurer's books will have a GGT value or even a complete data profile. This research investigates if statistical techniques such as imputation and supervised learning can be used in conjunction with available medical, demographic, underwriting and policy data to accurately predict GGT values. A combination of multivariate imputation by chained equations (MICE) and extremegradient boosted trees (XGBoost) offers a 31% improvement in accuracy compared to a naïve prediction. However, there does appear to be a limit to the performance achieved from all implemented techniques with the analysed dataset, with various model combinations yielding root mean squared error (RMSE) values within a narrow range. In addition, when comparing the predictions from a separate, unlabelled dataset to actual data, it appears as though predictions from the models cannot be reliably deemed to be from the same distribution. This indicates that further research is required before insurers can reliably switch out blood-work based GGT results for those from a supervised learning model. Keywords: insurance, underwriting, gamma-glutamyl transferase, imputation, supervised learning
author: Perumal, Yevashan
keywords: Statistical Sciences
date published: 2023
date modified: 2026-07-22T22:22:43Z
provider: University of Cape Town
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Department of Statistical Sciences

PropertyValue
property ID: Handle
value: http://hdl.handle.net/11427/39916