AI-Enabled Underwriting: The future of Insurance

It is strongly anticipated that a growing number of insurers will fast-track digital transformation as more and more insurers place significant bets on tech innovation. Along with large-scale and transformative investments in their front-office capabilities, insurers are turning their attention to underwriting.

Specifically, there is widespread consensus that the role of this central function can, and must, significantly change if insurers are to modernize their operations and infrastructure to realize customer value.

Underwriters – Heroes of the Future

Traditionally, insurance underwriting has been described as both an art and a science. But this definition of underwriting today is shaping as an art, science, and technology. If the trends keep up, the role of underwriting is to move from being a back-office function to a front-office function.

According to Aite Group’s report “Top 10 trends in Financial services, 2018”, a major trend in the global insurance industry will be the spread of unstructured data in Property and Casualty (P&C) underwriting and claims. Consumers want quicker underwriting and claims decisions, which will compel carriers to turn to unstructured data.

Endless External Sources

The proliferation of third-party data sources is reducing insurers’ dependence on internal data. Digital “data exhaust” from social media and multimedia, smartphones, computers, and other consumer and industrial devices—used within privacy guidelines and assuring anonymity—has become a rich source for behavioural insights for insurance companies, as it has for virtually all businesses.

Recently, the release of previously unavailable or inaccessible public-sector data has greatly expanded potential sources of third-party data. Also, with much better access to third-party data from a wide variety of sources, insurers can pose new questions and better understand many different types of risks.

With information streaming in from both traditional and non-traditional sources, underwriters can today access lengthy histories and view a more dynamic, complete picture of applicants. Equipping underwriters with tools to access and analyse this additional information is enabling the development of accelerated solutions. Applicants can be underwritten, and policies can be renewed, dynamically, based on their behaviors over time, in a more friction-less, consumer-friendly process.

Improved Risk Assessment

Underwriting involves analysing and evaluating the potential risks involved in the process of ensuring applicants and their assets. This is carried out based on the information provided in the application by the applicants, which usually has no assurance for accuracy. In a scenario when illegitimate information is fed, or the client purposely indulges in omission, the essence of the process is invalidated.

Integration of Natural Language Processing (NLP), a subset of machine learning in the underwriting process has enabled underwriters access to reveal more information about a potential client for improved risk assessment. Essentially, effective risk assessment translates to better pricing decisions for insurable risk which proves itself profitable in the long run to both the insurers and clients.


Enhanced Efficiency of Processes

The drawback of a human-based system is that human beings are inherently prone to errors. However, digitally dependent operations are invariably more accurate and efficient than human operations – Machine learning algorithms assess and check for errors present in information on the system much quicker than an individual doing the job.

Moreover, ML algorithms also eliminate the need for third-party operations between the insurer and the insured, leading to enhanced customer relationships for better products and services. In totality, an error-free system implies accurate information, leading to proper information evaluation and subsequently more efficient customer service.


Real-time Analysis to ensure Customer Value

Real-time Analyses and Visualization are fundamentally changing the relationship of insurers and the insured. By letting insurers to their behaviour, customers can learn more about themselves. Insurers can leverage this data to optimise their operations and influence behaviours. Millions of dollars of venture-capital investments in innovative Insurtechs are spawning the development of new and more sophisticated tools.

Moving forward, big data and the availability of non-traditional data is leading underwriters into the new reality: streams of information continuously collected and subsequently fed into algorithms and machine learning tools that convert that data into immediate, actionable insights. The result will be multi-dimensional, interactive, and predictive view of risk that will change the way we look at underwriting.

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