AI and Predictive Analytics in Underwriting, 2022 Advancements in Machine Learning for Loss Prediction and Customer Segmentation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P111Keywords:
Insurance Underwriting, Predictive Analytics, Loss Prediction, Customer Segmentation, Gradient Boosting, Deep Learning, Telematics/Iot, Computer Vision, Shap, Fairness, MlopsAbstract
In this paper, the authors of this review report the material development of the insurance underwriting process in 2022 with references to artificial intelligence (AI) and predictive analytics and the application of machine-learning techniques to predict loss and customer segmentation. In addition to generalized linear models, carriers are using gradient-boosted trees, random forests, and deep neural networks frequently in frequency-severity or Tweedie models, where nonlinearities and heavy tails and heterogeneous risk among policyholders are necessary. Such models were based on more valuable data pipelines, which comprised structured policy/claims histories, geospatial peril layers and telematics/IoT streams with unstructured evidence (adjuster notes, inspection pictures) processed with NLP and computer vision. Calibration (isotonic/Platt) and quantification of uncertainty (quantile/ensemble methods) increased adequacy of the rate, referral thresholds, and survival models and large-loss gates increased tail estimation. To perform segmentation, representation learning and clustering (e.g., k-means, Gaussian mixtures, HDBSCAN) identified micro-cohorts based on expected loss, volatility and price elasticity, which made it possible to perform target pricing and risk-enhancing behavior that boosted conversion without compromising portfolio quality. More importantly, governance became more mature: SHAP-based explanations, fairness and drift audits, and practices (feature stores, registries, shadow/canary releases) internalized transparency and stability into deployment. Generalize reported advantages in error rates and run time and map integration strategies of real-time scoring and human-in-the-loop inspection and open problems data quality and proxy bias, model drift, regulatory constraints, and compute/latency trade-offs and future directions in multimodal, causal, and explainable-by-design models
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