Hyper-Automation in Insurance: What Does It Mean and What Are Its Upsides?
During these unprecedented times, significant challenges have ascended to the top of the insurance industry’s agenda and left many carriers puzzled about how to navigate the road ahead.
Insurers need to focus on retention of their legacy of data and relationship-based selling while at the same time adjusting to a world with a growing millennial workforce and the rising digital demands that come with that.
To address these challenges, the answer for insurance companies lies in hyper-automation.
What Is Hyper-Automation?
From a technology perspective, it’s the continuous integration of automation into business operations using artificial intelligence (AI) and machine learning (ML).
From an insurance perspective, it is the key to businesses remaining viable in an industry that is growing increasingly more competitive.
There are many factors that have emerged and brought with them a new urgency to the insurance industry and the need for hyper-automation.
Some of the key drivers include:
1) Increased Competition
These businesses have given policyholders new choices, specifically when it comes to exploring other providers offering customized products and services.
For incumbents, this represents a threat, and to retain existing customers while onboarding others, they must begin offering delightful experiences.
2) Operational Efficiency
Insurers are grappling with marginal growth and reduced interest rates. If this wasn’t bad enough, their loss ratios have also substantially increased, which has further reduced the operating margin.
In response, insurers must leverage the business and technology nuances to address the challenges.
3) Millennial Demands
In the U.S., 22% of the 328 million population — almost 72 million — are millennials who expect a flawless customer experience when engaging with a brand.
According to Zendesk, 53%of millennials said they cared more about the quality of a company’s customer service in 2021 than they did the previous year.
4) Elastic Imperatives
In crises, there is often a surge in customer service requests in specific categories and a downturn in others.
This was on full display during COVID-19 when, for example, automobile accidents dropped while there was an increase in business interruption service requests.
To succeed, insurers must have flexibility in both process, and products to scale up or down in accordance as needed.
Now that we know the drivers, where does a company begin the “reimagining the business model” journey?
The answer is the core functions.
Today a major portion of an insurance company’s costs come from its core functions. For example, in its “Digital disruption in insurance: Cutting through the noise” report, McKinsey estimates that property and casualty and life and annuity carriers derive about 30% to 40% of business costs from the top 20 to 30 core end-to-end processes.
Today, many of these processes are running on legacy platforms and are slow to digitize.
This is where digitization and hyper-automation efforts need to begin.
In these areas, speed is critical, and hyper-automation delivers in several key areas, including:
1) Document Ingestion
Teams can process the documents received through various channels, extract, recognize and interpret the information and context using self-learning models to hand over for further processing accordingly.
Downstream processes can also be automatically routed to the requestor, providing the human representative with information to provide timely and accurate responses regardless of volume.
2) Accelerated Underwriting
Traditionally, underwriting takes time, so much so that many prospects end up leaving for other companies that offer better deals and faster response times.
Hyper-automation auto ingests applications, using intelligent models that triage the applications and assign them to the underwriter.
This has made insurers almost 10X times more efficient.
The process gets even more interesting when underwriter workbenches integrate with multiple data sources, which provides pre-scoring analytics on the applications.
This enables the enterprise risk managers to get a view of the impact on the overall exposures and powers an interactive experience where underwriters, enterprise risk managers and brokers can collaborate and accelerate the quotation cycle time.
3) Claims Automation
A policyholder’s claims experience can determine their loyalty to their insurer.
With hyper-automation, the auto ingestion of claim documents, intelligent segmentation and assignment are all automated. In addition, low-value and high-value claims are auto-adjudicated, and payments are made instantaneously.
This simplifies the entire process while making it more efficient and consistent.
Insurtechs, such as Lemonade, have used these technologies to achieve hyper-automation, extending their market share.
What’s Making Hyper-Automation Possible
At this point, let’s examine the innovative elements of hyper-automation that are making all of this possible.
As with other industries, technological advances have been the catalyst to business model transformation. Hyper-automation integrates many of these technologies together to offer a connected experience that have made it possible for insurers, intermediaries and insured to adapt to the changing landscape.
The services for structured data (i.e., customer profile) or unstructured data (i.e., social media interaction) range from data cleansing, transformations, migration and use of data lakes and data marts.
Industries are also experimenting with synthetic data generation to create AI/ML models that bypass the long cycle time to procure real field data.
In hyper-automation, this data and the various data services are pivotal to the success of the business model evolution.
Analytics is a term very widely used that overlaps with multiple different data functions.
In this context, we specifically call out the reporting-related analytics, trends and patterns observed from the business intelligence perspective. This includes descriptive analytics, predictive analytics and prescriptive analytics based on statistical models.
The various aspects of analytics in insurance are: marketing analytics, agent or broker analytics, channel analytics, product analytics, underwriting or pre-scoring analytics, risk analytics, claims analytics, call center analytics, customer experience analytics, compliance analytics, etc.
Advances in AI, ML and deep learning models using neural networks have evolved, allowing insurers to learn from historical data and blend that with multiple data sources and analytics.
The data sources could come from third parties, IoT-enabled data from health bands or automotive vehicles, social data from social conversations, and more.
AI examples in real applications today include risk assessment, product recommendations, premium calculation, loss estimates and fraud identification, to name a few.
Insurers are working with multiple providers to achieve the connected experience using hyper-automation.
In P&C home insurance, businesses use devices to proactively alert insurers and policyholders about events such as leakage in pipes, possible flooding, required services for the home and equipment.
In fleet insurance, devices can provide insight into the distance, travel, duration of the day and driving patterns —valuable information that can be used for underwriting, policy servicing and claims.
We cannot talk about hyper-automation without mentioning process mining and process optimization.
These are the precursors to process automation. It began with business process modelling, which evolved to the use of business rules and more sophisticated AI models that provided the next-best-action in the process.
Processes mining is an analytical discipline for discovering, monitoring and improving processes, by extracting insights from event logs.
The big buzz words here are “digital” and “cloud.”
While the cloud is viewed as the key to reducing total cost of ownership, infrastructure costs, and the like, the advances in devices and device-based computing are what bring in a distinction between what gets done in the cloud and what gets done at the edge. Today insurers are leveraging these through their use of APIs/microservices wrapped to the legacy core systems.
Looking Ahead to Innovation
With these business drivers pushing the envelope for insurers and the technology enablers bringing together a connected experience, hyper-automation is going to put insurers in the position to revolutionize the industry.
The response of the insurers to the pandemic is a great example of the resilience and adaptability of the industry. &