Overview of Performance Analytics in the Insurance Industry

Abstract

Research Question: This study explores various methodological approaches used to evaluate performance analytics in the insurance industry from 2020 to 2024, with the aim of improving efficiency. Motivation: Modern business operations and activities in the insurance domain face multiple challenges arising from dynamic regulatory changes, digitalization, and growing business competition. In this context, performance analytics represents a key means designed to enable the evaluation and improvement of efficiency, productivity, and decision-making processes. Although multiple scientific studies have addressed this topic, a significant lack of systematic literature analysis covering the last five years is evident. In this research, by combining bibliometric analysis and content classification, a thorough analysis of recent literature was carried out to highlight the variety of methodological approaches and compare their different applications in the insurance domain. Understanding the specific uses of performance analytics enables decision-makers to better optimize their resources, reevaluate and improve their strategies, and become more competitive in the field. Idea: The core idea of the study is to provide a structured overview of the scientific literature published from 2020 to 2024, mainly focusing on different methodologies used to evaluate performance, such as DEA and SFA, while also examining the input and output indicators used, as well as different efficiency measures. Data: The analysis covers 84 scientific papers collected from relevant databases (Web of Science, Scopus, ScienceDirect, Google Scholar) for the period 2020-2024. The data include key methods, used indicators, application areas, as well as regional and institutional distribution of the research. Tools: A designated Python script was written and used to extract key bibliometric data for the analyzed scientific papers. In order to visualize those data, specialized software VOSviewer was used, along with Excel, which was mainly used for statistical processing. Findings: Most of the papers use DEA method, its variations or combinations, with technical efficiency and productivity being the most frequently measured aspects. The most common inputs (operating costs, capital) and outputs (net profit, investment income) were identified. Chinese institutions lead in terms of both the number and impact of publications. Non-DEA studies mostly focus on corporate governance and the impact of ESG factors. The methodological diversity and the importance of performance analytics for strategic decision-making in the insurance sector are highlighted. Contribution: The paper provides a comprehensive review and comparison of performance analytics applications in the insurance sector over the past five years, covering methodologies, input-output indicators, areas of application, and institutional contributions.