Applications of data mining in the financial sector of the insurance sector
Commercial use of data mining. Data mining process involves using a large relational database to identify the correlation in certain data. The main role of the applications is to sift the data to identify correlations. A discussion on the acceptability of data mining. The aim is to make it easier for researchers in the financial field to use data mining methods and to expand its application scenarios used by researchers in the computer field. This review introduces the principles and steps of decision trees, support vector machines, Bayesian, K-nearest neighbors, k-means, expectation maximization. Necessary. In insurance language: risk management. is a tool that identifies business opportunities to design and. change the insurance products. The risk can be serious. impact if that is not the case. Text mining technologies have significantly affected the financial sector. As data has grown tremendously in every financial sector, text mining has become a major area of research in finance. Therefore, reviewing the recent literature on text mining applications in finance can be useful for identifying areas for which data mining methods such as clustering and outlier analysis and characterization are used in financial data analysis and mining. Below are some cases in the financial world where data mining is used. 1 Forecasting loan payments. Data mining methods such as attribute selection and attribute ranking analyze customer payment. Data mining is important in retail because retailers can analyze this data, develop targeted marketing strategies, optimize inventory management, and improve the overall customer experience. Some top applications include inventory management, sales forecasting, and supply chain optimization. Application of data mining in banking and finance With the advent of digitalization, the banking sector processes and manages enormous amounts of data and transaction information. Data mining applications in banking can easily be the suitable solution as they can identify patterns, victims, market risks and others. Application of data mining based on swarm intelligence algorithm in financial support of livestock and poultry breeding insurance DOI: 10.21203 rs.3 .rs-2699428 v1Dynamics of big data applications in the insurance sector. In the cited field we found the publication pattern and intellectual organization. To the best of our knowledge, this study is the first to use bibliometric analysis to summarize the use of big data in the insurance industry. On the said topic we have again answered: Career in Data Science and its Future Growth. 5. Personalized services. Through consumer analytics and consumer data management, companies analyze buyers' choices and the types of products they are interested in. Data Science Use Cases in the financial world make this process much more hassle-free. The key driver of digitalization in the insurance sector is The increasing adoption of smartphones and cheap high-speed internet connections in India. This has led to the creation of new opportunities for insurers to connect with their customers through mobile apps, social media platforms and other online websites and portals. Natural Language Processing, NLP is an AI technique that is making its way into a variety of vertical markets, but the financial sector is one of the most interested in the business.