AI in Insurance: Enhancing Fraud Detection and Risk Assessment

Abstract

An important development in risk management and fraud detection is the banking industry's use of artificial intelligence (AI). The revolutionary implications of AI in various fields are examined in this research, with an emphasis on the benefits and difficulties associated with its use. AI has a wide range of effects on risk management. Traditional approaches to detecting fraudulent activity and evaluating risks are becoming inadequate as financial transactions get more intricate and sophisticated. AI technologies provide a revolutionary answer to these problems because of their ability to analyse enormous volumes of data at previously unheard-of rates. The use of AI in financial services is examined in this study, with an emphasis on how it might improve risk management and fraud detection. This study explores the many ways artificial intelligence (AI) may be used to identify, stop, and handle fraud in the banking industry. In order to control risk and make wise judgements, the insurance sector has historically depended on actuarial science and historical data. However, a paradigm change in predictive modelling has been brought about by the development of artificial intelligence (AI) and machine learning (ML) algorithms, opening up new avenues for risk assessment and management. This research advances our knowledge of machine learning's role in transforming risk assessment techniques in the car insurance sector via thorough investigation and synthesis. Additionally, Natural Language Processing (NLP), where AI examines textual data from several sources to verify client identity, may be used to improve Know Your client (KYC) procedures. By visualising transactional interactions, graph analytics provides a distinctive viewpoint and may draw attention to questionable practices such quick money transfers that might be signs of money laundering. By combining a variety of data sources, predictive analytics goes beyond conventional credit scoring techniques to provide a more thorough understanding of a customer's creditworthiness. This flexibility includes cross-channel analysis, IoT integration, and phishing detection, offering a thorough protection against complex fraudulent efforts.