Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

In the insurance industry nowadays, data is carrying the major asset and playing a key role. There is a wealth of information available to insurance transporters nowadays. We can identify three major eras in the insurance industry's more than 700-year history. The industry follows the manual era from the 15th century to 1960, the systems era from 1960 to 2000, and the current digital era, i.e., 2001-20X0. The core insurance sector has been decided by trusting data analytics and implementing new technologies to improve and maintain existing practices and maintain capital together. This has been the highest corporate object in all three periods.AI techniques have been progressively utilized for a variety of insurance activities in recent years. In this study, we give a comprehensive general assessment of the existing research that incorporates multiple artificial intelligence (AI) methods into all essential insurance jobs. Our work provides a more comprehensive review of this research, even if there have already been a number of them published on the topic of using artificial intelligence for certain insurance jobs. We study algorithms for learning, big data, block chain, data mining, and conversational theory, and their applications in insurance policy, claim prediction, risk estimation, and other fields in order to comprehensively integrate existing work in the insurance sector using AI approaches

Automated extraction of attributes from natural language attribute-based access control (ABAC) Policies

Abstract The National Institute of Standards and Technology (NIST) has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy (NLACP) to a machine-readable form. To study the automation process, we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations. Therefore, this paper focuses on the questions of: how can we automatically infer the hierarchical structure of an ABAC model given NLACPs; and, how can we extract and define the set of authorization attributes based on the resulting structure. To address these questions, we propose an approach built upon recent advancements in natural language processing and machine learning techniques. For such a solution, the lack of appropriate data often poses a bottleneck. Therefore, we decouple the primary contributions of this work into: (1) developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts, and (2) generating a set of realistic synthetic natural language access control policies (NLACPs) to evaluate the proposed framework. Our experimental results are promising as we achieved - in average - an F1-score of 0.96 when extracting attributes values of subjects, and 0.91 when extracting the values of objects’ attributes from natural language access control policies