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Effect of Model Drift on Insurance Claim Prediction Accuracy

Abstract

Predictive models are increasingly used in insurance to estimate claim frequency, claim severity, and the likelihood of future insurance losses. However, the statistical relationships captured by these models may change over time because of changes in policyholder behaviour, economic conditions, claims patterns, product structures, or other underlying factors. This phenomenon, commonly referred to as model drift, can reduce the accuracy and reliability of insurance claim predictions when models are not appropriately monitored and updated. This study will examine the effect of model drift on insurance claim prediction accuracy. It will assess how changes in the underlying characteristics of insurance data over time influence the predictive performance of insurance claim models. The study will also examine differences in prediction accuracy between models affected by drift and models that are regularly monitored or updated using more recent claims experience. The study will focus on model drift, insurance claim prediction accuracy, predictive models, claims experience, prediction error, model performance, policyholder data, claim frequency, claim severity, data distribution changes, model monitoring, and actuarial prediction. Statistical and predictive modelling techniques will be applied to identify changes in data patterns and assess their effects on insurance claim predictions. Model performance will be evaluated across different periods to determine how changes in underlying data characteristics affect prediction accuracy. A quantitative research approach will be adopted for the study. Historical insurance policy and claims data will be analysed using predictive modelling techniques, statistical validation, drift detection measures, prediction error analysis, calibration assessment, and performance comparison. Model predictions across different periods will be compared with actual claim outcomes to determine the extent to which model drift affects prediction accuracy. The study is expected to reveal that model drift may have a significant effect on insurance claim prediction accuracy. Changes in claims experience and underlying policyholder characteristics may cause previously developed models to produce less accurate predictions over time. The extent of prediction deterioration may depend on the magnitude and speed of data changes, the type of predictive model used, the quality of available data, and the frequency with which model performance is monitored. The study will be useful to actuaries, insurance companies, underwriters, data analysts, risk managers, regulators, and researchers. It may provide useful information for detecting model drift, improving claim prediction accuracy, strengthening predictive model monitoring, and determining when insurance models require recalibration or redevelopment. The findings may also support more reliable actuarial and data-driven decision-making in insurance. The study concludes that model drift is an important consideration in insurance claim prediction because changes in underlying data patterns can influence the reliability of predictive models over time. It is therefore recommended that insurers and actuaries regularly monitor model performance, assess changes in claims data, and apply appropriate recalibration or model updating procedures when significant drift is identified.

Keywords: Model drift, insurance claim prediction, prediction accuracy, predictive modelling, claims experience, prediction error, model performance, data distribution, claim frequency, claim severity, model monitoring, model validation, actuarial prediction, model recalibration, insurance risk.