In 2020, Elon Musk predicted that robots would be able to do everything better than humans by 2025. While he may have been pretty ambitious with that timeline, there’s no denying how far robots and AI have come, and at such a fast pace.
Bold predictions like Musk’s have those of us in insurance wondering what transformation artificial intelligence (AI) and machine learning (ML) can bring to our industry. A 2025 takeover of the market by AI isn’t likely, but insurers around the world are placing AI/ML near the top of their list of strategic priorities even though it’s often unclear where to start or what to focus on.
Adoption of AI/ML among insurers will continue to accelerate as more tools become available from insurtechs and through platforms like AWS and Microsoft Azure. To unleash the full potential of AI/ML, there are two prerequisites for insurers. First, there needs to be a clear problem that can be solved with an objective, actionable insight. Second, there needs to be a massive amount of data that systems can ingest, analyze, and learn from to create those objective, actionable insights.
When it comes to the lifecycle of an insurance policy, it’s not difficult to identify friction points and challenges. While data intensive activities like fraud detection have already seen AI/ML make a measurable impact, new opportunities will emerge as intelligent technologies mature. What follows are four use cases in which insurers will be applying AI/ML to automate processes entirely or empowering humans to make better decisions.
Visual claims: Accelerating resolution and improving customer experience
A recent survey found that 68% of all insurance policyholder complaints were related to claims. Filing an insurance claim has notoriously been a sticking point, involving an excessive amount of time for both the insurer and the insured. Visual claims help insurance companies solve this issue by enabling customers to engage via a mobile app and camera.
The experience is fully interactive and provides policyholders with a faster, more straightforward claims process. For example, in the case of a car accident, the customer can communicate remotely with the insurance company by sharing real-time video footage. Agents or digital/AI agents can assess factors like the vehicle damage, weather conditions, skid marks, signage, and position of the vehicle. The process gets “smarter” over time as ML solutions learn from data and identify patterns.
Because all imagery and videos are captured and delivered digitally, an on-site inspection by the insurer is no longer required — thus saving time and money. Through use of AI-aided analysis, claims payouts and repair services can even be triggered automatically upon loss.
According to McKinsey & Co., AI technology will reduce the overhead associated with claims by 70% to 90%, compared to 2018 levels.
Accelerated underwriting and instant issue policies to improve the customer experience
One frustration that customers have with all types of insurance is the belief that the application process takes too long and involves too many steps. Accelerated underwriting solves this by using automation, data, rules, models, and pathing to make underwriting decisions in a much more condensed timeframe.
By using the power of APIs to ingest more types of data from a slew of new sources (such as social, credit, behavioral, economic, wearables, IoT sensors, financial, and identity) insurers can make better risk decisions. Coupling this data with algorithms, artificial intelligence, predictive analytics, robotic process automation (RPA), and cognitive computing enables insurers to accelerate those decisions and, in many cases, provide instant-issue policies.
The highest-performing underwriting groups will be those that successfully blend advanced analytics with human judgment. Potential benefits for accelerated underwriting include:
- Increased sales as more applicants recognize the advantages of a faster, more frictionless application process
- Faster decisions powered by low touch/no touch decision trees, which will improve the customer and agent experience
- More consistency as cognitive computing enables data-driven decisions and reduces the points of where potential manual failure can occur
- Improved loss ratios enabled by a data-rich environment that allows for more granular and accurate risk classifications and pricing specificity
Digital agents: Accelerating resolution and process efficiencies for routine tasks
In commercial and personal lines of insurance, one of the biggest impacts of AI will be the rise of digital agents. While the traditional agent will still have an important role to play, AI-powered agents will handle the majority of omnichannel interactions with customers, freeing human call center agents to focus on activities that require a personal touch. For example, requests for policy information, adding a new driver or changing coverage are perfect use-cases for voice-AI and app-driven engagement.
Meanwhile, human agents will primarily focus on complex sales, claims, and billing situations, with human interaction bolstered by analytics and data-driven insights. The percentage of consumers who prefer to submit claims via mobile rose 77% from 2018 to 2021, according to PwC.
Parametric insurance: Accelerating resolution and process efficiencies for claims
Parametric insurance has been around since the 1990s. Unlike traditional policies, a parametric policy pays out when a specific event exceeds an agreed upon threshold, rather than on an actual incurrence of loss. This essentially eliminates the claims handling process, enabling insurers to gain significant cost savings.
While most commonly associated with coverage for natural catastrophic events, intelligent and cognitive technologies enable a wider range of use cases including protection for:
- Cities and airports hit by a terrorist event
- Hotels and cruise ships in the event of an infectious disease outbreak
- Agri-businesses and shipping companies when water levels fall
Issuing parametric insurance on a broader scale will require more robust indexing on a variety of data being created and captured both in real time and over time. Advances in IoT sensor technology, predictive analytics, and artificial intelligence (AI) will enable insurers to collect and analyze data on a variety of phenomena to make these risk decisions possible.
Why insurers can’t ignore their core systems
The ability to apply AI and ML to any of these use cases inherently depends on data. This is true because to be effective and accurate, AI and ML need to be trained regularly with a vast amount of quality data in order to detect patterns and create intelligent output.
Legacy core systems were never built for today’s data intensive world. They were predominantly built on monolithic architectures with closed technologies. In contrast, modern coretech platforms like EIS CloudCore are built for open, high-velocity innovation. This gives insurers the freedom and flexibility to connect to any data source they require for AI/ML use cases.
As we enter a new age of insurance, it’s clear that the winners will be decided not just on the success of their business execution but on the wisdom of their technology choices.
How EIS ClaimSmart can help
EIS now offers ClaimSmart, a market-leading solution to simultaneously stop claims leakage and improve customer trust throughout the claims process.
You can learn more about ClaimSmart here, but in a nutshell, it’s comprised of two key parts:
- ClaimGuard is powered by AI and machine learning algorithms to constantly enhance its own ability to identify issues in the ever-evolving world of fraud, saving insurers millions.
- ClaimPulse, on the other hand, revolutionizes claims automation for more efficiency and customer transparency. This significantly reduces LAE, and eliminates a lot of unnecessary manual work.
To learn more about ClaimSmart, download the ClaimSmart product overview right here.
