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. Large amounts of data are known as big data as a result. Volume, Velocity, and volume are three characteristics of big data. 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.
This paper discusses different techniques and specialized procedures which can be used to effectively protect data from the owner to the cloud and then to the user. The next step involves categorizing the data using three encryption parameters provided by the user, which are Integrity, Availability, and Confidentiality (IAC). The data is secured through various methods such as SSL and MAC protocols to ensure data integrity checks, searchable encryption, and splitting the data into three parts for cloud storage. Dividing the data into three portions not only enhances security but also facilitates easier access. Access to the encrypted data requires the user to provide the login information and password of the owner. This paper also studies critical security issues like unauthorized servers, brute force attacks, threats from cloud service providers, and loss of user identity and password.
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