By Farooq Ahmed Farid | 27 Oct 2021

How do actuaries and the insurance industry benefit from using data science?

Data science is an increasingly influential pillar of the insurance industry. In what ways does it help insurers provide a better offer to their customers?

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Big data and data science is an increasingly influential part of daily life.

At an individual level, it powers the content we see on social media, the movies streaming services recommend to us, and even the timing of notifications and emails we receive.

Elsewhere, data science is increasingly used in healthcare planning and delivery, by political parties and organisations, and by sports teams.

Data science is also becoming an increasingly influential pillar of the insurance industry, helping actuaries within insurance, reinsurance, and underwriting companies make smarter decisions, benefitting themselves and their customers.

Here are some of the key outcomes and benefits that data science can deliver to actuaries and insurers.

1.    Better products for consumers (and better targeting of those consumers!)

Using data science to harness the potential of big data makes it much easier for insurers to develop relevant products for their target customers. One of the most significant changes over the past decade has been the significant growth in data available from different sources. So while we would consider social media and a wearable app to be two completely different types of tech, they can be equally vital in helping to develop better insurance products for consumers.

In the health insurance space, wearable apps, in particular, have a vital role to play. For example, data from Fitbits and other smartwatches are already used by some insurers to provide incentives and plans to users, both on an individual and collective level. In this context, data science can aid the production of more comprehensive products that better serve the needs of consumers.

On top of these benefits, data science can also help insurers do a better job of putting their products in front of their desired audience.

2.    More accurate risk assessments = better underwriting and personalised pricing

The potential for data science to enhance the insights available to actuaries and underwriters is likely to be the most significant long-term gamechanger in the insurance industry.

It’s unlikely that the processes behind generating health insurance quotes, for example, will change too much, but the outcomes for customers almost certainly will.

In the first instance, utilising data science enables insurers to assess applicants' risk profiles in a more granular level of detail. Access to this data should lead to smarter underwriting decisions and premiums that align more accurately with the actual level of risk posed by the insured.

This data could lead to:

  • Insurers being able to provide more comprehensive levels of coverage
  • Better tailored plans to individuals’ needs and their underlying health indicators
  • Cheaper or more expensive premiums based on a fairer risk assessment
  • Insurers not taking on too much liability by accepting applicants deemed above a specific risk level

The most notable outcome is that health premiums will become further grounded in fairness, and people increasingly start getting quotes reflecting their personal circumstances.

As technology continues to develop, data science may also make it easier to generate quotes for applicants. Not only will this lead to a better experience for consumers, but it will also make insurers more productive and minimise fraud risk.

3.    Stronger long-term engagement with customers

At present, many insurers have little to no engagement with their customers.

Processes like renewals, applying no claims discounts, and even claims management are often automated.

However, insurers are already increasingly using data science to engage directly with customers to incentivise them to follow a healthy lifestyle. The use of wearable tech to track exercise and activity is one such trend. As mentioned earlier, many insurers offer lower premiums to customers who are evidently doing more to safeguard their health.

Eventually, such trends will likely evolve along similar lines to our first point about better-tailored products. Insurers will be able to rely on real-time data to suggest changes customers might want to make to their plans, whether that's adding or removing features or benefits or taking advantage of added-value services.

4.    More robust claims management processes

Finally, data science will continue to make claims management more robust, delivering benefits for customers and insurers in equal measure.

From a consumer perspective, the use of data science alongside artificial intelligence will mean that straightforward claims can be processed and settled quickly. As a result, insurers won't even need to engage with these claims, instead focusing on more complex claims and those that require human assessment.

In this context, machine learning will become an increasingly vital tool alongside data science and AI to ensure these processes continuously evolve and improve.

Data science: The future is NOW

When discussing data science or innovations in general, it’s always tempting to look ahead to the benefits these will bring in years to come.

While the use of data science will undoubtedly continue to evolve in the coming years, it is already playing a vital role in helping innovative insurers develop better products and pricing for their customers. Such insurers are likely to become market leaders throughout the next decade, as their agility leaves them better placed to take advantage of data science and other technological developments across cloud and digital platforms.

By Farooq Ahmed Farid

Farooq is a qualified actuary, a risk & underwriting management professional, strategist and data scientist. During his career, Farooq has been instrumental in helping to develop international best practices across underwriting, technical pricing, actuarial, analytics, product development, risk management, reinsurance, and business intelligence in order to implement successful strategic business plans.

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