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Use Cases for Machine Learning in Insurance

Sep 21, 2021 | 8m

Gain actionable insights into:

  • Using a predictive approach as adoption of machine learning increases
  • New data sources and supporting infrastructure that needs to evolve
  • Making improvements for each step of the value chain

Data Disrupted

Data science might be getting more attention these days, but it has always been a key part of the insurance industry. Actuaries were the original data scientists, and insurance companies have used advanced statistical models to price and assess risk for decades. And the insurance industry is changing fast. Disruptions from advancing technology and computational capabilities are allowing for wide adoption of machine learning (ML.) This is complemented by the type and nature of data insurers are now able to acquire. Increasingly, insurance companies are not only relying on decades old data like mortality tables, but also using dynamic models leveraging data collected in real time.

How much value ML can bring to your organization depends greatly on the foundation – people, organization, availability and quality of data. Before implementing advanced ML models, organizations need to make a fundamental shift in how employees are trained, how assessments are made, as well as how data is collected and processed across all aspects of operations. Beyond ML, it’s developing organizational skills and processes that will make a real difference.

This Power Read will offer an overview of ML, how it impacts the insurance industry and a look at some of its applications to date. Let’s look at where and how data is acquired and how this, in turn, reshapes the models and processes that currently anchor the industry.

Past and Future

Historically, the information insurance companies have on customers was captured just once, usually when the customer first purchased the policy. This information was not necessarily refreshed, especially for life products, even with decades having elapsed.

Compare this with a bank’s relationship with their customers. Like insurance companies, bankers need to get fairly detailed information when customers apply for a credit card or a loan. The captured information is used similarly to insurance: to assess the risk associated with the customer, and this information is then stored as part of the customer’s profile. Due to the nature of services offered, banks engage with their customers on a regular basis. Even if the relationship is largely transactional, they continue to learn a little more about the customer’s behaviour and preferences with each interaction. As insurers, we may not see customers for years and usually reconnect only as they need to claim, move to a different life stage, or seek to reassess their protection coverage.

Traditionally, and for the most part now too, insurers use details from these infrequent interactions -- a combination of the static history submitted years ago and other details such as demographics tying a customer’s risk to their generic profile – to predict future outcomes.

ML allows insurers to stop looking at customers solely through the lenses of the past data, even with a relatively small number of interactions. With more information available, insurance companies can move away from just providing indemnification. Instead, we can develop predictive models that can be used in prevention. Through the growth of ecosystems (links to healthcare, for instance) more interactions and information can be captured, to build better quality and more accurate models.

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