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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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Insurance policy approval and decline

Introduction

AI and ML have already started to be applied in various industries and the insurance industry is not an exception. The conventional methods of underwriting insurance policies have been greatly enhanced with the coming of these technologies. This way, instead of relying on individuals to make decisions based on their intuition, AI and ML algorithms can consider a large number of factors and make precise conclusions. Not only does it increase the rate of approval but also the efficiency and accuracy of the whole process. Consequently, there are prospects and challenges when it comes to the adoption of AI and ML in the insurance industry.

Challenges

There are several difficulties in the process of integrating AI as well as ML in the insurance sector. The first challenge that can be identified is the problem of data, both in terms of quality and quantity. This is a challenge because AI and ML models need a lot of good quality data for them to work properly and this is something that insurance companies that have not invested in good data management systems are likely to struggle with. Secondly, there is the problem of bias. It is possible for AI and ML algorithms to reinforce biases present in the data sets if they are trained with them. Thirdly, there is the problem of transparency and interpretability. The decisions of AI and ML models are not always clear, and it makes insurance companies struggle to explain their choices to the clients and regulatory authorities. Finally, there is the problem of legal consideration. The application of artificial intelligence and machine learning in the insurance sector is highly regulated and hence there are legal requirements that insurance companies must fulfill when using such technologies.

AI Solutions

There are however challenges that one is likely to encounter in the use of AI and ML in the insurance policy approval and decline process. For example, predictive analytics can help in determining the risk appraisal to a certain standard, while natural language processing can be used to handle written claims. In addition, there are various machine learning models that can be employed to analyze claims history and make better forecasts in the future. Currently, there are companies such as Zest AI that is creating AI models that are supposed to eliminate bias and increase efficiency. More so, there are other firms such as Shift Technology that is applying artificial intelligence in the detection of frauds in claims thus minimizing expenses for insurance organisations. As for the regulation, AI can be leveraged to perform the monitoring and reporting of compliance automatically thus easing the workload of employees.

Benefits

There are a lot of advantages of using AI and ML in the insurance sector. Firstly, the use of these technologies can greatly enhance the efficiency of the policy approval and decline process thus enhancing the satisfaction of the customers. Secondly, they can enhance the reliability of risk appraisal thus resulting in better pricing of the policies. Thirdly, they can also decrease the costs by performing repetitive tasks and identifying fake claims. Finally, they can assist insurance organisations in meeting the standard regulations in the best possible way.

Return on Investment

There are significant return on investment (ROI) for the adoption of AI and ML in the insurance sector. Based on a research done by Accenture, AI has the potential of reducing costs in the insurance industry to $300 billion by 2025. Also, a research conducted by Capgemini revealed that AI has the potential of creating $1. 6 trillion of economic value for the insurance industry by 2035. These statistics indicate that the ROI of AI and ML in the insurance sector could be high.

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