Auto ML Pipelines for Real-Time Underwriting Risk Scoring
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I4P102Keywords:
Automl, Underwriting, Risk Scoring, Real-Time Analytics, Insurance, Machine Learning PipelinesAbstract
The rapid advancement of machine learning has significantly influenced the insurance industry by enhancing underwriting processes through more efficient and accurate risk assessment. In this paper, we provide an automated machine learning (AutoML) pipeline that performs time underwriting risk scoring, feature selection optimization, model adjustment and implementation without the support of a large number of people. The suggested framework brings together data preprocessing, feature engineering, model selection, and real-time scoring to make risk scores that are easy to understand and work quickly. Our tests on a large insurance dataset suggest that our method would be better at predicting outcomes and have less lag time than a conventional underwriting process. The practice will help insurers make better decisions, which will lower both the financial risk and the problems with customer service
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