This article (page 56 -58) written by Softelligence founder and CEO, Adrian Blidarus, explores the impact of Artificial Intelligence (AI) in underwriting, including the opportunities and challenges associated with the use of AI. AI refers to computer systems that perform tasks that mimic human intelligence, while Machine Learning (ML) is a subset of AI that focuses on improving how computers infer and predict based on data. The five main areas where AI is applied in underwriting are risk assessment, fraud detection, automation, personalized pricing, and predictive modelling.
AI can help underwriters analyze vast amounts of data to identify patterns and assess risks more accurately, even those that may not be obvious through traditional underwriting methods. It can also be used to detect fraud by analyzing data and identifying patterns consistent with fraudulent activity. Additionally, AI can automate many underwriting processes, such as data entry, data verification, and risk assessment, which can help to reduce errors, save time, and improve the overall efficiency of the underwriting process.
Another important use of AI in underwriting is in personalized pricing, where AI analyzes individual customer data to create more personalized and dynamic insurance policies and pricing models. However, the implementation of AI in underwriting also raises concerns around data privacy, cybersecurity, and ethics in AI. Thus, it is important to properly set up AI on a well-designed foundation, and ensure that the data used in the process is reliable and that it complies with relevant laws and regulations.
In summary, AI offers significant opportunities for underwriting in terms of accuracy, efficiency, and personalized customer experiences. However, these benefits must be balanced against potential risks and challenges, and underwriters need to approach AI implementation in a pragmatic and realistic manner.
