Becoming a data-driven insurer of the future

Your directory to tapping the digital insurance revolution

In the post-data-big-bang world we live in, the expanding digital universe continues to reshape the insurance industry at an unprecedented rate. First movers with the foresight to harness the astronomical volumes of data are well-armed to disrupt and displace organisational laggards. The internet population has grown tremendously to include over 3.7 billion unique users (or 51% of the human population) and is estimated to generate 2.5 quintillion bytes of data daily. ⁱ This is enough data to fill 10 million Blu-ray discs, that if stacked, would be 4 times as tall as the Eiffel Tower.

Figure 1

Insurers – incumbents and InsurTechs alike – are aware of this data fortune and how it, together with technological advancements could revolutionise their ways of working. As our recent global survey of insurance executives from Singapore, Malaysia, Indonesia and Thailand demonstrates, a majority of insurers acknowledge that AI will change the way they gather customer information. ⁱⁱ Personalised targeting, improving services via robotic process automation, and cost optimisation are such some examples of how leveraging AI can bring a 15 to 25 percent uplift in revenue.Whilst this is not a zero-sum game, the size of the pie could, shrink dramatically simply because of the first mover’s advantage. Tailoring premiums based on customer needs to increase lead conversions, or reducing exposure to the insurer by tagging a suitable price based on improved insights on a customer’s medical risk, or incentivising customers’ healthy lifestyle and safe driving behavior by way of access to fitness trackers or vehicle telematics (e.g., Manulife and Progressive Insurance), first movers are already riding this tidal wave of change in many areas. Why exactly, and how are they doing it?

Revamping the entire insurance value chain

Data and analytics have the capacity to impact the entire insurance value chain, propelling insurers to adopt digital ways of working in order to reap rewards. The use cases for data and analytics enhancing an actuary’s task of exposure reduction while creating more product opportunities are bountiful in Asia. Here are some Examples:

• Prudential Finance now offers HIV-positive customers life insurance policies, thanks to statistical evidence of their longer life span

• AIA Life Malaysia has streamlined underwriting to less than an hour via tailored, specific profile-based questions on a digital platform

• Ping An and Bought By many identify travel insurance whitespaces by harnessing Chinese travelers’ social media data to understand unmet demands

• Zhongan utilises AI-driven image recognition to remotely but accurately detect whether a phone screen is really cracked, or simply photoshopped, to mitigate claims fraud

The opportunities are numerous and crystal clear.

Changing customer expectations

Beyond the obvious advantages to evolve, pressure to change also comes from changing customer expectations of their insurers. Three types of customer personas emerged when assessed against the four dimensions of demand for cost, customer service, trust and digital innovation, namely:

1. “Digital Nomads” – highly active digitally, and ready for digital innovations,

2. “Hunters” – are more concerned about value for money and appreciate a human touch, and

3. “Quality Seekers” – seeking quality and responsive services. ^iv

With new customer personas emerging, insurers must respond with personalisation – by replacing one-size-fits all offerings with bespoke solutions for distinct customers. ^v To achieve this, insurers must transform into a data-driven organisations, leveraging data to drive these innovations.

Figure 2

In this age of digital, where customer needs are ever-changing, insurers are required to become increasingly agile to thrive in an increasingly competitive market. Here are the three main things for an insurer to get right in order to do this successfully.

Getting the right data

The amount of data generated today is monstrous, but not all of it is relevant or worth tapping into. Finding and leveraging only the right segments is essential for insurers to refine and redefine their product offerings to golden standards. For example, vehicle telematics data would be tremendously useful for an auto insurer, but much less so for a health insurer.

Figure 3

The first and most important step of the journey to becoming a data-driven insurer is therefore to get the right data. Forming digital ecosystem partnerships is a great way to sustainably and effectively capture more diverse types of data. ^vi A beautiful example is the partnership between Grab in this region and the insurance company, Chubb, where drivers can conveniently purchase personalised, need-based car insurance through their Grab app (e.g., pay-per-ride). With the telematics data collected off Grab drivers (including Grabike data, which Grab has already started collecting in Jakarta), Chubb will be able to further refine its insurance products specific to driving behaviours that may be unique to the country or region.

Building the right end-to-end data architecture

Once the right data sources have been identified, the next step is providing the necessary infrastructure to support the transformation. The key data architecture components can be better illustrated through the graphical journey below:

Figure 4

	Ben’s client, James, posts on social media announcing the arrival of his newborn   Ben receives an instant push notification through his tablet due to publishing API to the client’s social media platform and real-time data ingestion and event processing.
	Ben proceeds to open the dashboard from his tablet and is able to view the client’s portfolio The client’s portfolio is presented on the BI dashboard and fed with insights from analytics models used to predict James’ lifetime value, forecast his churn rate, assess his monthly ability to pay, and understand his needs based on existing records before customising insurance plans catered specifically for him.
	Equipped with data insights about his client, Ben is recommended to give James a call The Next Best Action (NBA) model recommends calling James as it’s his preferred channel of communication-based on previous records.
	Ben accesses previous client call records to double-check the client’s phone number Data visualisation is necessary for Ben to have access to the call records that reside in a remote system, whilst master data management ensures up-to-date information and a single source of truth, and data security confirms that Ben has the right to get hold of James’ number.
	Armed with relevant information displayed in a user-friendly format, Ben makes the call With every interaction Ben has with his client, including notes made on the tablet, data is stored in a data lake. The higher the volume of data received, the readier and more equipped Ben will be for the next interaction with the client. This also means having a central storage repository that is capable of high data ingestion processing velocity - both structured and unstructured.
	The call ends on a pleasant note as James agrees to buy a life insurance policy for his newborn The deal is quickly closed via the call as James authenticates himself via biometric recognition (fingerprint and voice) and due to straight through underwriting leveraging existing customer data. Data is fed back into the data lake to allow the actuary to refine future products in a safe sandbox environment known as the data lab.

Adapting your organisation

The right data and architectural foundation are incomplete without the right people. Training programs that enable employees to learn new analytics skills and utilise data in their day-to-day work must be introduced to prepare existing talents for a transformational shift. At the same time, insurers may need to bring new talents into the organisation with new roles such as UX designers, data scientists and data engineers. New minds bring fresh perspectives, ideas and ways of working, inspiring writing a data-driven culture and mindset into the DNA of the organisation.

Creating the data-driven insurer of the future

Figure 5

Many insurers already recognise the need to change and evolve into a whole new type of organisation to prevent disruption. ^viii They are investing in better management of data within their organisations and in developing and sourcing key talent. Notwithstanding, many insurers in this region, especially life insurers, still lack the capabilities needed to effectively leverage data within their own organisations, much less the wider stream of data coming from unconventional sources. Since a one-size-fits-all approach does not exist, building the necessary ecosystems, partnerships. Infrastructure and company culture will be crucial in enabling insurers to become truly data-driven. The time to walk the talk is now, putting idea into plan, and plan into action, before the dividends become too small.

END

ⁱ Bernard Marr, How Much Data do We Create Everyday? (Forbes, 2018).
ⁱⁱ Accenture Technology Vision for Insurance (Accenture, 2017).
ⁱⁱⁱ Accenture’s insurance analytics benchmarks
^iv Accenture 2017 Global Insurance Distribution & Marketing Consumer Study, Voice of the Consumer (Accenture, 2017).
^v Accenture Insurance Consumer Survey
^vi Accenture Technology Vision for Insurance (Accenture, 2017).
^vii Accenture Technology Vision for Insurance (Accenture, 2017).