Today, underwriting is tedious, time-consuming and inconsistent. With AI, we can realise a better tomorrow.
Leveraging AI to revolutionise underwriting
Underwriting is at the core of the insurance industry. Today, however, the process is tedious, inefficient and leads to inconsistent results.
Human intervention in underwriting is a significant factor, such as with life insurance. Of course, where people are involved, inconsistencies are inevitable.  Many insurers are further hindered by old technology and procedures that prevent them from leveraging Internet of Things (IoT) tools like smartwatches and telematics. These devices provide real-time data on the activity level and driving performance of clients.
The solution to these problems is underwriting powered by artificial intelligence (AI). With AI-powered underwriting, decisions can be reached in days, not weeks. Since AI can derive insights from historical data, decisions can also become more consistent and reliable.
A few insurance companies are using AI in select areas, but there is yet to be widespread adoption of AI solutions across the underwriting landscape.
There are four techniques under the broad spectrum of AI that would provide underwriters with a comprehensive solution and a competitive advantage:

Machine learning
Natural language processing
Deep learning
Behaviour data models

Machine Learning
Insurance providers have vast amounts of historical data on applicants – medical history, family history, job data – as well as the risk assessments underwriters assigned to applicants based on the available data and a static set of rules.
A machine learning algorithm is capable of ingesting that historical information and creating a model that will imitate the decisions underwriters have made to that point.
For simpler cases, allowing the model to assign the risk classification would move the industry towards zero-touch underwriting, where decisions are made with zero manual intervention from the underwriter.
For more complex cases, the underwriter’s decision can be made quicker and with sounder judgment because of the underlying model that is

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