Out of every crisis, the insurance sector has only emerged stronger. The current crisis is no exception. After a slump, global insurance markets are bouncing back. McKinsey confirms a gradual rebound in 2021, while Deloitte forecasts much-awaited growth in premiums for 2022.
Source: McKinsey — Global Insurance Report 2022
Cross-sector recovery and market normalizations (to an extent) are driving organic growth for insurance players.
At the same time, pandemic-prompted digital transformations in the insurance sector have put some insurance companies in better starting positions for the new growth cycle. But many insurance technology adoption efforts have also stalled or been sidetracked because of uncertainty, rising risks, and pressing operational priorities (like transferring entire teams to remote operations).
In 2022, insurers are once again back to the drawing board. This time around, many have bigger technology budgets — but also a larger spectrum of needs to address.
If I were to drop by your board meetings today, I’d probably hear things like:
- We need to improve our digital customer acquisition process. Do we have a UX designer, or who’s in charge of this?
- Why is our claims management process so slow? Should we invest in robotic process automation? I’ve heard good things about that.
- Let’s use AI to better predict new risks and adjust premiums. Who knows an AI guy?
- Why aren’t new customers buying from us? What’s wrong with them picking up the phone to call?
- Blockchain! I heard someone saying we should do “blockchain,” no?
Indeed, technology for insurance is easy to find these days. You have plenty of options pitched by tech vendors, startups, consultancy firms — practically every thought leader in the space.
But how do you move away from decades of separation of business and technology toward an interconnected digital insurance stack?
We discuss three essential tips in the next section:
- Define your business objectives
- Critically assess hyped technologies
- Build your digital insurance strategy from the bottom up
Digital insurance: Why not all technologies were born equal
Similar to financial services at large, the insurance industry is:
- Data-dependent
- Susceptible to risks
- Interwoven with other industries
Insurers need to do three major tasks in parallel: improve customer offerings and the customer experience, minimize the risk radar, and grow operational effectiveness. Many must do so across service lines designed for different industries — automotive, healthcare, logistics, construction, etc.
Because there are so many jobs to be done, there’s no shortage of technology for insurance being branded as “promising,” “innovative,” or “disruptive” for one use case or another.
Source: InsurTech Insights – Insurtech 2022: Hype vs. Impact
Artificial intelligence (AI) and machine learning (ML) are promoted as solutions for improving back-office processes: claims management, underwriting, risk analysis, etc. At the same time, they are touted as the tech pillar for innovative insurance products — context-aware, usage-based, and hyper-personalized.
Likewise, drones in insurance can massively increase the speed and quality of inspections while supplying field data during claims investigations.
What is often left unsaid in conversations surrounding new insurance technology are prerequisites for adoption.
Can most insurers benefit from AI? In the long-term perspective, sure. Will every insurer get immediate ROI from AI? Not without mature digital infrastructure in place.
Much-hyped emerging technologies for insurance require an existing technical foothold — cloud connectivity, APIs, robust and secure data governance frameworks, and a high degree of business process automation.
When an insurance business is mostly backed by a paper trail rather than connectivity, no novel technology can fix that. You need to put down your digital roots first.
And we propose a bottom-up approach for that:
Baseline: Digitize standard business processes
Branches, brokers, paper-based back-office — the triumvirate worked well, with small improvements being enough to spur growth.
But year after year, the baggage of legacy insurance systems has gotten heavier. Though some players have managed to successfully digitize some processes and update the system’s legacy rules, productivity improvements have often proved marginal.
McKinsey found that among global P&C carriers, expense ratios reduced only by 45% between 2014 and 2019. IT and operations accounted for 50% of a typical insurer’s cost base in the same period.
Source: McKinsey — Successfully reducing insurance operating costs
For most insurers, the idea of modernizing legacy core systems sounds almost heretical. Why flip on its head what is fully functional? you might be wondering.
And you’re right. Legacy insurance systems don’t have to be ripped apart or fully replaced with new custom solutions. In most cases, you’ll end up with another obsolete system in several years’ time.
But you can’t keep the status quo either. Inaction can lead to loss of market share and revenue to faster-moving digital insurance players that offer a better customer experience (CX), release innovative products faster, and operate at lower costs.
What’s the fix, then? Progressively adding new blocks atop your core systems and digitizing back-office and front-office processes.
To achieve extra operational resilience and contain costs, insurers must map:
Let’s take claims management as an example. Six in ten insurers admit that emerging technologies have made a significant impact on their claims processes. Why? Because most of the steps have been manual and/or dependent on human labor.
Steps from paper-based claims submission to subsequent data entry, verification, and claims settlement are done semi-automatically (using rules) with few integrations available for faster data reconciliation.
When essential steps like intake information collection, data dissemination, and reconciliation are automated with RPA, astounding results follow. A multilinear insurer automated their disability claims management process (one of many) and saw a 25% increase in staff productivity, a 40% reduction in handoffs, and an estimated $37.4 million project cost reduction over four years. They didn’t even have to retire any legacy software, by the way.
Action steps:
- Digitize standard business processes
- Implement RPA and intelligent automation
- Move extra systems to the cloud
- Break down data silos for operational reporting
- Establish a data governance framework
Mid-term: Leverage digital insurance technologies for added value
You’ve done the groundwork. Your back-office runs like clockwork — business systems are better integrated, third-party data comes via integrations, and standard workflows are automated. Agents are happier and more productive, plus expense ratios are down.
It’s time to push the envelope further and face the customers.
The new generation of connected consumers don’t want to visit the branch or talk to an experienced agent on the phone.
Insurance customers are more thrilled by the prospects of
- 54% getting good value for their money
- 48% convenience of obtaining services
- 24% fairness of the policies offered
All the above are pillars of a stellar customer experience (CX) in insurance — but providers don’t hold up to their end of the bargain. Most overestimate their CX compared to the competition:
Source: IBM — Elevating the insurance customer experience
The majority of insurers also harbor outdated beliefs about customers. In 2020, only 5% of insurers actively used websites for engaging with prospects, whereas 49% of consumers marked this channel as “important.”
It appears that cost containment and operational effectiveness improvements pushed customer-focused initiatives to the backburner over the past two years. If that describes you, it’s time to get back on the customer track. ,
Where traditional insurance companies have stalled, InsurTech players have rapidly moved in. Around 40% of digital insurance companies have cut into profits at the marketing and distribution segments of the insurance value chain.
Similar to digital banks, InsurTech providers have won over customers with convenience and fast access to competitively priced products. Using a combination of intelligent process automation and big data analytics, they can issue flexible policies in minutes. A seamless digital account opening experience and fast onboarding have helped them rapidly grow their user bases.
In the turbulent 2021 market, InsurTech startups secured a record-setting $19.8bn in funding. That’s a 176% increase compared with 2020.
As it’s easy to guess, a lot of these funds will go into further CX improvements and diversifying digital insurance service portfolios.
Traditional players must move fast too and transition to omnichannel sales, plus deploy more tailored customer products.
Place customer pain points at the center of your insurance product development. Then ask how good your company is at recognizing these issues during the sales process. What steps are you taking to increase the speed, accuracy, and fairness of claims resolution?
When mulling these questions, many insurers realize they lack sufficient data. In particular, many lack intelligence across two axes — customer knowledge and field data.
| Customer knowledge | Claims-related data |
|---|---|
| Standard customer journeys | Unstructured customer-reported data |
| Purchase decision factors | Pre-existing health conditions |
| Changing life circumstances | Historical data from past inspections |
| Price sensitivity | POS consumer data |
| Psychographic information | Geospatial intelligence |
| Habits and behavioral data | Mobile interactions data |
All of the above comprise big data in insurance — raw intel you can transform into insights for instantaneous decision-making, powered by advanced analytics solutions.
These insights are also essential to the development of parametric insurance products, where historical claims data is used to determine a set of conditions that trigger an instant settlement for a client. At the same time, big data analytics solutions can improve your agents’ productivity and diminish risk exposure for your business (without raising premiums for customers).
Take it from one of our clients, a global life insurer. In two years, we helped them set up an advanced risk assessment portal for streamlined customer assessments. The system connected to multiple data sources and took into account a wide number of parameters, including a customer’s medical history, sports activities, occupation, travel, and residence.
Powered by a dynamic neural network, the portal enabled insurance agents to complete health and lifestyle evaluations using scientific guidelines and proven methods for risk assessment in healthcare.
Action steps:
- Implement digital customer channels
- Start accumulating customer intelligence
- Implement big data analytics for selected use cases
- Look for extra data integrations via APIs
- Embed data analytics into new customer products
- Decide which hardware can help you gain missing insights
Long-term: Technologies for transforming insurance business models
Customer-focused, digital-driven operations today, adoption of emerging technologies tomorrow — that’s the principle insurers should operate by.
All of the technology trends in the insurance industry require a spectrum of IT architecture transformations to deliver the promised results:
But the good thing is that once you deal with the bottom of the iceberg and prepare the base, there are few to no blockers on your way to pursuing innovation — and getting tangible returns on tech investments.
Here are four technologies that can unlock new profit pools in digital insurance.
IoT devices
IoT devices are compact, low-energy sensors that can be attached or embedded into different gears for data collection. With the help of IoT and edge devices (sensors with computing capabilities), insurers can collect:
- Environmental data: Temperature, pollution levels, moisture level
- Movement data: Latitude and longitude, cardinal direction of movement
- Health data: Daily activity levels, heartbeat, blood pressure
- Property data: Movement detection, energy use, thermal efficiency
All of this data can then be incorporated into parametric and/or personalized insurance products — and used for faster claims management. Also, extra data enables insurers to predict and mitigate risk by proactively notifying policyholders about possible mishaps. For example, based on thermal efficiency data, you can notify commercial building owners about possible system malfunction before it manifests in an accident.
Similarly, IoT data can be extremely valuable for cargo insurance, as it lets you track the exact transport conditions of goods and notify shippers of mishandling. This use case is particularly promising for cold chain logistics, where precise transportation conditions are crucial.
Telematics
Telematics data, collected by many automotive companies, can supply insurers with knowledge about customers’ driving behavior (which is the top cause of accidents). With this knowledge, insurers can offer lower premiums to careful drivers and establish claims liability.
For instance, connected insurance startup Flock offers on-demand car insurance products, where coverage kicks in once the vehicle starts moving. The team relies on telematics and geolocation data to track vehicle use and driving behavior in real time. Then it bills the customer once the ride is over. This degree of flexibility is attractive for occasional drivers who don’t want to pay for a monthly/annual policy, as well as for the growing ecosystem of shared MaaS asset providers.
On-board vehicle diagnostics and performance metrics can also help insurers design more attractive products for commercial fleet managers and logistics companies. Apart from personalizing policies to drivers’ behaviors, companies can leverage telematics data to suggest maintenance — and thus reduce the volume of preventable insurance cases.
GIS data
A geographic information system (GIS) helps to collect, analyze, and visualize geospatial data. For P&C insurers, GIS is a more comprehensive alternative to human-led inspections. Geospatial data also comes in handy for predicting natural risks and conducting large-scale assessments of affected areas.
In 2020, 70% of insured losses from natural events were driven by small to midsize severe weather events. GIS enables insurers to predict and plan ahead for such risks (and adjust premiums accordingly).
GIS data can also help prevent crop insurance fraud. Using infrared imagery, insurers can verify and measure vegetation growth without in-person visits — and offer better premiums to farmers.
AI and ML
All of the above emerging technologies supply better data to insurance providers. AI and ML algorithms help translate it into targeted actions.
The most high-value use cases of AI in insurance include:
- Risk prediction. ML and DL-based systems can perform complex multivariate analysis of various risk factors to provide agents with high-level and granular insights for decision-making. You can perform both historical analysis and future outcome-based modeling to decipher how various factors will affect tracked risks. Then you can adjust your premiums and customer offerings accordingly.
- Underwriting. By developing individual buyer profiles based on collected internal and external data, insurers can make instant underwriting decisions and adjust pricing. Automated underwriting can also help build personalized quotes and policy coverage for buyers to improve conversion rates.
- Claims management. Intelligent automation can streamline claims routing and validation by automatically filling in data and verifying its accuracy. Natural language processing (NLP) and optical character recognition (OCR) can also help process unstructured data inputs from clients to increase review speed and accuracy. At the same time, algorithms can provide more accurate loss estimates (using device-supplied data) to ensure fair claims settlement.
AI technologies have a strong potential to transform the insurance sector. So it’s hardly surprising that 74% of insurance leaders plan to increase spending on AI projects this year according to Deloitte. But almost an equal amount (72%) also plan to invest more in cloud computing and storage, while 69% are ramping up their data acquisition and processing capability.
The reasoning is simple: you don’t grow a tree from the leaves down. Without fertile soil and sufficient room for developing a strong root system, you can’t grow an orchard.
Similarly, digital insurance transformations should be established from the bottom up. Prepare the grounds first — establish cloud connectivity and address gaps in existing IT architecture. Then help your root system develop — optimize and digitize existing business processes.
Next, shift to the front office and look into digitizing customer channels and establishing a robust customer data collection pipeline. Cultivate it further by integrating data from third-party sources via APIs.
Once that’s done, you’re ready for cross-pollination. Identify gaps in operational knowledge and determine which emerging technologies could help you fill them. Finally, pull your new digital insurance system together with AI algorithms to cover the remaining manual steps in standard workflows.
Contact Intellias to receive a personalized assessment of your digital maturity level and growth levers.
