Abstract

Within the insurance field, the digital revolution has enabled the collection and storage of large quantities of information. This era is referred to as “big data”, since the great uncertainty to be modelled is too complex for traditional data processing techniques. For insurance purposes, big data refers to unstructured and/or structured data being used to influence underwriting, rating, pricing, forms, marketing and claims handling. The use of artificial intelligence (AI) in actuarial science is a rapidly growing area that is significantly impacting the insurance industry. This integration of technology involves both machine learning (ML) algorithms and data analysis to help actuaries develop more accurate risk assessments and predictions. With a larger possibility of accessing huge sets of data, actuaries can leverage AI to automate underwriting and claims processing, improve customer experience and provide valuable insights to insurers to perfect decision-making strategies. In this way, AI has the potential to transform traditional actuarial methodologies and provide customized solutions for individual clients. In particular, the field of insurance has greatly benefited from the application of data science. With the use of data analytics, insurance companies can now develop predictive models to better manage risk, underwrite policies more accurately and determine valid pricing. Data science is also used to automate specific procedures such as claims processing, fraud detection, and customer retention. Insurance companies may use data science to identify and build new products that better match clients’ needs and preferences. In addition, the accessibility of data can improve operational efficiency by identifying areas of improvement and revenue-generating opportunities. Overall, the use of data science is quickly becoming a crucial aspect of the insurance industry, helping insurers improve their decision making, create new products and services and enhance customer satisfaction. This Special Issue covers methodologies and methods focused on the application of data science in the insurance and financial context. Owens et al. (2022) focus on Explainable Artificial Intelligence (XAI) (see Clinciu and Hastie 2019; Barredo Arrieta et al. 2020), which refers to the development of artificial intelligence systems that provide a clear and concise explanation of how their decisions are made. The demand for the production of more transparent models, the need fpr techniques that allow for humans to interact with them and the trustworthy inferences from such transparent models are the main justifications for the development of XAI. Starting from this consideration, the authors provide a systematic review about current applications of XAI within the insurance industry, which will contribute to the interdisciplinary understanding of applied XAI. Sriram et al. (2023) tackle data preparation and cleaning by providing a novel application based on AI techniques and ML system architectures. In particular, the authors focus on policy listings data that pose their own unique challenges, and develop a holistic AI-based platform that standardize, improve and automate the data preparation of insurance through machine learning. Secondly, a rule-based, pairwise corporation entity resolution framework is provided that allows standardization of insured entities, enabling policy aggregations. Neural networks have been exploited in Flaig and Junike (2022) and Jose et al. (2022). Generative adversarial networks (Goodfellow et al. 2014) are applied in Flaig and Junike (2022) to expand the scenario generation process to a complete market risk calculation for Solvency II purposes. The study shows that the proposed approach can represent a viable alternative method for market risk modelling beyond traditional economic scenario generators, which can also serve as regulatory-approved models, as they perfor
Lingua originaleEnglish
pagine (da-a)N/A-N/A
Numero di pagine3
RivistaRisks
Volume2023
DOI
Stato di pubblicazionePubblicato - 2023

Keywords

  • Data science in Insurance

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