The new age insurance: Personalisation with AI & data analytics applications

AI and data analytics have unlocked new opportunities in the insurance industry. Learn what thought leaders have to say on the key trends like usage-based insurance and customised claims handling.

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  • Advances in AI and data analytics are shaping the next level of insurance personalisation, paving the way for usage-based insurance and customised claims handling. 

  • Insurers’ product propositions are transforming thanks to an explosion of new data sources, from connected sensors to open-source data ecosystems. 

  • The new age insurance of the future will be a multidimensional one, drawing together varied channels, technologies, and scenarios seamlessly to enhance customer experience.

An AI revolution is dawning on the insurance industry, and it looks set to light our way to a new age of hyper-personalisation.

You’re likely familiar with the popular use case of AI-powered chatbots – bots that can recommend personalised products and help customers file claims. But this is only the tip of the iceberg. Not far off lies a future where we can instantly purchase policies tailored to our individual habits, where health premiums may be based off our routines or where IoT sensors in our cars can detect accidents and trigger automatic claims.

Currently, only 15% of insurance companies and banks globally deploy AI extensively across their organisation. But by 2025, 86% say they plan to boost AI-related investments in technology. Concurrently, the global insurance analytics market is set to grow at a CAGR of 14.2% between 2020 and 2027, as more insurers tap into predictive analytics to enhance customer relationships.

As AI advances, it will take insurers far beyond simply streamlining existing functions. In an increasingly data-driven world, AI will enable insurers to understand their customers more deeply – birthing all-new product categories and next-level seamless experiences.

These trends were discussed during a panel on AI & data analytics applications in the new age of insurance personalisation, at the 2nd Insurance Analytics & AI Innovation Asia Pacific 2022. Hosted by Kaushal Silva, Client Business Solutions APAC at Zühlke, the panel brought together thought leaders in the use of data and AI.

Here’s a breakdown of the key insights:

Personalisation: The key to the digital future of insurance

Today’s consumers are open to giving insurers personal data on their behaviour and habits, but only if they benefit from personalised offers and attractive discounts. Shifting expectations call for insurers to issue policies and finetune pricing in a more dynamic, granular way.

Driven by AI and data analytics, two key trends are taking shape:

1. Usage-based insurance aligns customer behaviours with the premiums they pay. Dubbed "pay-as-you-go", this model first gained traction in the auto industry, enabling drivers to pay based on miles clocked. 

But with the rise of telematics, insurers today can tap into data not only on drivers’ mileage but also their travel routes and driving habits. As AI tools receive real-time data from IoT sensors, they can dynamically adjust premiums to, for instance, reward cautious driving behaviours – unlocking a new frontier of personalisation.

2. Customised claims handling draws on automation to streamline the once-lengthy claims process.  

Kristin Warne, Head of Life & Health Claims and Business Management Client Services at Swiss Re, shares that automation leads to reduced administrative burdens and a better experience for both customers and employees. 

“In today’s era of digitisation, customers still deal with paperwork for life and health claims. Now, customers expect to be able to lodge claims digitally and have a personalised experience,” Kristin says. 

“By tapping into internal data sources, customers won’t need to re-enter relevant information in a digital claims form. Questions can also be more tailored to the customer's product and circumstances. Coupled with technology that validates information from other sources, claims assessment becomes more transparent and faster for less complex claims. Companies can then spend an appropriate amount of time engaging with customers for more complex claims.”

In fact, predictive analytics can potentially move insurance toward a proactive rather than reactive approach to manage risk – for example, by pre-emptively alerting customers of potential faults in their home. 

In the event of actual property damage, for instance, smart sensors can trigger claims seamlessly and transmit all necessary data for advanced algorithms to validate claims.

As customer habits shift post-pandemic, insurers who offer AI-driven personalisation will be best placed for success. A 2022 survey by TransUnion and J.D. Power found that consumers are driving less post-pandemic, yet paying the same or more in traditional coverage. This drove 49% of consumers to opt-in for a telematics-based programme. 

The challenge for insurers lies in creating the right incentives for consumers to willingly allow collection of their personal data. To achieve this, insurers must add value by enhancing the customer experience. 


Enhancing customer experience through the multidimensional linkage of technology, channels, products, and scenarios

The new age insurance of the future will be a multi-dimensional one, drawing together varied channels, technologies, and scenarios to deliver holistic customer experiences. 

Forward-thinking companies are already embracing digital innovation. Singapore insurance co-operative NTUC Income offers SNACK – an app that provides users with free insurance coverage when they shop with SNACKUP brands. This flexible form of micro-insurance creates ongoing, value-added experiences for customers across various lifestyle scenarios.

Another notable example is Manulife MOVE, a customer rewards programme designed to encourage healthier habits. Enabled by technology-like wearables, Manulife can track and reward users as they walk, run, and clean. By integrating insurance solutions into daily habits, the programme helps customers enhance their health-related quality of life. 

Challenge ideas workshop

Common pitfalls in deploying advanced analytics and AI

As insurers dive into the uncharted waters of AI, implementation challenges are par for the course. According to Kaushal Silva, Client Business Solutions APAC at Zühlke, insurers face three main pitfalls today:

1. IT/Business misalignment 

AI transformation calls for a company-wide strategic shift, but many insurers today lack the right mindset to move toward a data-driven culture.

“Insufficient end-to-end collaboration between business and IT teams can create a poorly thought-out business case,” Kaushal explains.

“IT may be pushing AI adoption, but their solutions are not aligned with the business team who are closer to the clients and know what they want. On the flip side, the business team may have an amazing idea, but IT is unable to bring out its business value. Companies will need to align between business and IT.

2. Poor data quality and management 

AI solutions are only as good as the data you feed them. Many insurers struggle to unlock the value of their data effectively. Only 22% of insurers say they have at least a consistently accessible set of core data to build models, and up to 75% lack a taxonomy to harmonise diverse types of data.

Incomplete or poorly consolidated customer data threatens insurers’ ability to detect claims fraud. Without well-organised data on customer habits and behaviours, insurers will also face difficulties in providing accurately personalised services.

Effective data flow and governance across organisations and those they work with is essential for shared decision making and efficiency gains. Learn more about data ecosystems and how they can solve your data quality problems.

3. Lack of maintenance 

AI transformation does not stop at deployment – it involves continually evaluating and retraining machine learning models to account for changing customer data. One key mistake that insurers make is launching a great product offering – then neglecting to maintain their ML models after.

Constantly iterating can be a challenge, so Kaushal encourages insurers to plug into ecosystems to get access to entire networks for technical expertise.

“Your best solution for customers today will be irrelevant in a couple of weeks,” says Kaushal. “The whole point of customer centricity is constantly adapting to customer needs. Companies need to see AI not as a one-time investment but an investment through the cycle.”

Business Innovation

The new data sources transforming product propositions

One immediate reason for insurers to start investing in advanced analytics and AI is speed. 

Nelson Chan, Head of Financial Services Business Development for Hong Kong and Taiwan at Amazon Web Services, highlighted three tangible benefits for insurers from the "speed" enabled by advanced analytics and AI.

"Firstly, insurers can obtain usable data faster with real-time processing of data streams. Secondly, insurers get actionable insights faster from data that’s continually analysed and available to their data scientists and business intelligence teams. Finally, insurers can execute faster with both rapid business case approval and cloud-native, agile teams using DevOps and application programming interface architectures."

Three key areas of opportunities for investment that Nelson predicts are in lifestyle or behavioural insights for better propositions and underwriting, enhanced customer experience in distributions and claims, and synced business and customer views. 

With these new areas for innovation, insurers will certainly need enhanced data management capabilities to keep up with the sheer explosion of consumer data.

The IDC forecasts over 55 billion connected IoT devices worldwide by 2025, generating a massive flood of insights into customers’ health, homes, and cars. For insurers, this means a decisive shift from dependence on internal, self-declared data to external, tracked data. 

Companies will be able to personalise pricing and customise claims processing through input from the connected sensors permeating every aspect of life: telematics, wearables, smart homes, even biometrics. As data analytics tools become more sophisticated, it will be possible to efficiently process unstructured data like social media activities and voice analytics too.

How will insurers obtain such data? McKinsey foresees the rise of open-source ecosystems that allow public and private entities to share data under certain regulations. Insurers can build partnerships with tech companies to create individualised products at a lower cost.

One intriguing example is Alipay’s free health insurance service. Offered in partnership with China-incorporated life insurance company Taikang, the service allows users aged below 60 to accumulate free insurance coverage by performing payments with their Alipay Wallet.

This partnership has proven wildly successful, with 13 million Alipay users opting in for coverage in the first 20 days after launch. In a new age of insurance, such partnerships can conceivably be leveraged to spot consumers’ changing purchasing patterns – for instance, purchases for a new home – and offer policies personalised to their needs.

With new data sources comes a pressing need to ensure responsible AI.

Only 35% of global consumers today trust organisations’ use of AI, making it essential for insurers to build trust among customers. Transparent communication, strict compliance with data privacy guidelines, and participation in AI governance initiatives like the Monetary Authority of Singapore’s Veritas consortium, must be the way forward for insurers.

“To build trust, we must balance the value we can give customers for their data with the assurance that the correct standards and security protocols are followed,” says Kaushal.

Insurers – a trusted life-partner in a pre-emptive, automated landscape

Will automation and AI make the human touch obsolete? Not at all. Though insurance is traditionally a low-touch business, insurers must continue to engage with their customers to secure strong customer loyalty. This engagement can come through exceptional customer service as well as a strong brand identity.

Even as end-to-end automation of insurance processes becomes possible, the value of the human agent cannot be overlooked. 49% of insurance customers would trust a human advisor when making claims, whereas just 7% would trust a chatbot. The agent of the future must be ready to deliver thoughtful customer support at such crucial moments – drawing on AI-enabled insights to personalise their service.

To stand out in an increasingly competitive landscape, companies will also need to harness the power of brand-building. An authentic brand purpose gives a sense of meaning to insurance products – enhancing the ultimate goal of adding value to customers’ experiences.

Keen to learn more about how your business can leverage AI and data analytics? Contact Zühlke today.

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