The Implementation of Machine Learning in The Insurance Industry with Big Data Analytics
DOI:
https://doi.org/10.59461/ijdiic.v2i2.47Keywords:
AdaBoost, Naïve Bay , K-Nearest Neighbor , Decision Tree , Machine LearningAbstract
This study demonstrates how Machine Learning techniques and Big Data Analytics can be used in the insurance sector. Due to various web technologies, mobile devices, and sensor devices, the amount of data in the insurance sector is currently growing daily. Insurance companies deal with large amounts of data from different sources. The quality and quantity of this data may vary, making it difficult for machine learning algorithms to accurately analyze and predict risk. Data preparation, cleaning, and processing can be a time-consuming and expensive task. Machine Learning plays a significant role in converting data into information. Because Machine Learning has the ability to learn from the input data and is a fundamental part of data analytics tools, it learns from data to provide new insights, predictions, and decisions from vast amounts of data. In the insurance sector, machine learning has a wide range of uses, such as customer segmentation, fraud detection, customer retention, claim processing, and claim review. As a result of this study, machine learning creates various prediction models for the insurance industry such as AdaBoost, Naïve Bay, K-Nearest Neighbor, and Decision Tree. As a result, Machine Learning is currently seen as a fundamental game changer for insurance businesses. The potential use of machine learning in insurance businesses will be further investigated by integrating big data tools.
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