Finance analytics in business : perspectives on enhancing efficiency and accuracy

Abstract

Insurance is one of the most important economic and the world’s most dynamically developing market. It is a very important segment of the financial market because insurance and the related protection against all risks play a fundamental role in managing financial resources. Decision-making in the insurance market is very complicated due to the various types of insurance, including life insurance, health insurance, property, accident insurance, car insurance and others. Each of them addresses different risks and needs. Insurance companies pay the insured losses incurred in case of any accident in these areas. For this purpose, many advanced techniques for modelling and analysing financial data are created yearly to monitor and improve the decision-making process in the financial sector. This ensures financial stability and risk minimisation for households, enterprises, organisations and the state. This allows companies and organisations to make conscious decisions, optimise their financial strategies and increase efficiency and accuracy in various financial processes. Many tools are available for risk analysis, some simple and others more complex, such as SWOT Analysis, Monte Carlo Risk Assessment Method, Value at Risk (VaR) Assessment Method, Risk Factor Analysis (RFA), Risk Matrix and many other classic tools. Recently, due to the rapid development of IT systems and, consequently, the method of collecting and storing data, including financial data, has resulted in the creation of modern tools for data analysis and collection. Traditional data types were structured and could be easily stored in a relational database. With the emergence of Big Data technology, new, unstructured data of unknown value are being collected, originating from various social media (Twitter, Facebook, LinkedIn, etc.), web tracking technologies or mobile applications or data from equipment with sensors. For some enterprises, this may be millions of terabytes of data. Such a huge amount of data contributed to the creation of innovative analyses, enabling the study of interdependencies between people, institutions, entities and processes and using the obtained conclusions with much greater precision than before. This allows for making effective decisions. Currently, the main innovation lies in having technologies and methods that allow the analysis of huge amounts of data and the analysis and formulation of conclusions based on unstructured data such as text data. This phenomenon allowed for a continuation of Turing’s assumption from the 1950s, in which he suggested that machines, like humans, can draw logical conclusions to solve problems or decision-making. [extract]