Insurance companies have always used rigorous analysis of available data to price policies competitively and effectively. However, as a lower engagement product obtaining accurate and up to date information has always been the insurers biggest challenge.

As digital technologies have revolutionised much of today’s economy, the large amounts of new data available provides deeper insights than ever previously available to an insurer. The more insurers can understand about their customers the better they are able to:

• Price policies effectively, keeping loss ratios down and improving business profitability
• Price policies competitively – improving customer satisfaction and growing customer base
• Predict needs and therefore improve customer experience and engagement

While this presents the greatest opportunity, those that are unable to capture and deploy this data effectively will struggle to remain competitive. Accessing these new sources of data and creating value from them will be critical to successful insurers in the next 5-10 years.

So, what are the opportunities and challenges with the new datasets that insurers can tap into?

The Strength of Traditional Data Sources

The core premise of the insurer is always that greater market penetration, enables greater distribution of risk and therefore events can be accommodated better by the insurer than the individual. This is one of the best risk management tools available and not something that is about to change. With this in mind the traditional ‘market level’ datasets and application data that are used for pricing policies will remain at the core – with new datasets used to optimise and adjust existing pricing models.

Alongside this, as insurance is closely tied to key life stages demographic data remains critical. It is one of the key determinants in identifying pricing optimisations and predicting suitable products. Accessing third party demographic datasets also provide far greater coverage and reduced privacy restrictions than some of the other datasets discussed in this article.

There is also an opportunity to use ‘traditional’ data sources at different points of the customer journey. In countries with a highly developed insurance market, the use of credit risk data to predict auto insurance risk is prevalent. This benefits consumers with better pricing while enabling insurers to reduce losses.

In Asia Pacific, while regulation has allowed this for some time, it has been slow to be adopted – in turn contributing to slower adoption of third party price comparison sites that champion the consumer cause.

Health Tech:

Research from Deloitte indicates that 70% of consumers would be interested in using technology to track changes in their health, receive alerts, transmit health data, and pay their medical bills. Against this backdrop, consumer health tech is experiencing rapid growth, and that is set to continue.

Combining the growing trend of consumers leading healthier lifestyles with newfound abilities to track their health habits creates an opportunity for insurers to develop closer engagement with their customers and in turn reduce premiums for lower risk customers. In practical terms, this means moving away from the old pricing model based on static rate tables, to a model based on dynamic information provided in real-time.

The challenge that remains from ‘Health tech’ is the length of time it takes to develop reliable ‘experience’ data that would enable effective predictive analytics. In the interim, the best applications of this new data source comes from expert scorecards that focus on developing customer engagement through rewarding healthy behaviour.

Auto Data:

Car insurance is the clear case where tracking data can be deployed to adjust premium pricing. Theoretically, insurers no longer have to rely on standard questionnaire responses about accidents, age and location to develop a risk profile. These can be augmented or even replaced by telematic data from tracking devices.

Insurers gain access to insights on driver behaviour such as their average speed compared to speed limits, or which time of day they drive. The result is a more complete profile for developing personalised car insurance policies that create value and a competitive advantage.

That said adoption has been slow – not least due to the expense of installing the technology in existing cars. Insurers are responding to this through moving from ‘black boxes’ installed in customers cars to apps that are able to collate location data on behalf of the insurer.

In the future, it may be possible for insurers to be able to access this information through directly plugging into existing data capture programmes to access a ‘mobility index’, where that data has been collected appropriately and the customer permissioned the use. At this point, the use of tracking data in auto insurance policies will become ubiquitous in determining policy price.

Mobile Device Data:

What’s clear in the examples above is the ‘mobile first’ mentality, where apps are used as the key method to engage customers with their insurance products. However, these apps can also be used to collate additional attributes on the users device and behaviour – not related to health or driving.

This will provide a wealth of information on consumers that was previously not accessible to an insurer, however the use and deployment of this intelligence, while offering huge opportunity must be treated with care.

Likely we will see rather than using this at the individual level, aggregations of this information can be used at the macro level as a ‘new-tech’ equivalent to the traditional market level information discussed at the top of this article – providing the latest ‘digital lens’ on an overall market trends.

New Data / New Products:

Finally, with the rise of new data comes the opportunity for insurers to offer new products. One of the fastest growing insurance products is personal data protection.

Insurers are increasingly recognising the opportunity to offer identity protection policies that provide services for remediation should your personal data be breached. By plugging into ‘dark web’ monitoring it is possible to provide early warning alerts as soon as individual or corporate data has been breached and deliver services that enable consumers to rapidly remediate issues.

This is just one example of how this data driven, mobile first environment presents opportunities for new insurance products that will deliver significant growth to the sector in the coming years.

Conclusions:

With so many new datasets available, insurers do need to place greater consideration on customers' privacy and security. However sourcing new datasets from outside the enterprise gives insurers the opportunity to deliver innovative products and services that offer convenience and value. Insurers who look beyond their current data sources can do a better job of segmenting markets, pricing risk, developing new products and ultimately building a long term partnership with their customers.

Alan Thornton

General Manager, Data Quality Southeast Asia