Today’s business leaders are dealing with unprecedented levels of disruption and uncertainty. The increased reliance on mission-critical technology tools and infrastructure focused on keeping businesses running has perhaps given executives a newfound appreciation for information technology (IT) and the people at the forefront of digital-driven change.
Today’s business leaders are dealing with unprecedented levels of disruption and uncertainty.
Focused on immediate concerns, organizations have prioritized establishing flexible and secure remote working environments and empowering employees with new ways of working, connecting and collaborating.
The increased reliance on mission-critical technology tools and infrastructure focused on keeping businesses running has perhaps given executives a newfound appreciation for information technology (IT) and the people at the forefront of digital-driven change.
The role of IT in responding to major crises has never been more important. Whether an enterprise can truly achieve business resilience is largely determined by its ability to adapt quickly to new and often unexpected developments. In short, agile IT operations are more critical than ever, especially for insurance companies.
When the immediate challenges subside, how will your company transition back to a steady state of operations and seize new opportunities?
Fraud Detection and Risk Prevention
Operating in an information-dependent industry, insurers are eager to find ways to monetize the data they have. Although artificial intelligence (AI) will help to derive insights, the true value of this data will be realized when combined with other emerging technologies.
Take the problem of fraud, for example. According to the FBI, the cost of insurance fraud in the United States (excluding health insurance) is estimated to be more than $40 billion a year.
Enter blockchain technology. To combat crime, insurance companies could effectively store claims information on a distributed public ledger that would enable them to better identify suspicious behavior and communicate it to the appropriate authorities.
Expanding the Data Ecosystem
The accuracy and predictive capabilities of AI-based algorithms will increase only if they’re trained on claims data from multiple insurance entities. This is where an ecosystem of insurance entities sharing anonymized data can result in more efficient processes that would benefit the entire industry.
Broadening the data ecosystem beyond insurers can also improve the customer experience. For example, by partnering with companies in industries such as ride sharing, utilities, credit cards and real estate, carriers will be empowered to source improved insights to ultimately understand their clients more deeply in the ways that matter most.
This will result in new product categories, more personalized pricing, and, increasingly, improved service delivery.
Now Is Not the Time to Play It Safe
What worked in the past is not good enough for insurers operating in today’s increasingly uncertain and volatile world. Now is the time for insurers to be innovative, not derivative. Now is the time to unleash the power of modern, digital technologies.
But no single digital technology, such as AI, will suffice. Rather, insurers must find creative and strategic ways of combining emerging digital technologies to expand their capabilities and to develop unique solutions to pressing issues.
For instance, life insurance ownership in the United States has declined from 63% in 2011 to 57% in 2019, according to a joint study by LIMRA, an industry trade association, and Life Happens, a non-profit educational organization. A primary reason for that 6% drop is that millennial families are not buying life insurance at nearly the same pace as previous generations; instead, many are deferring the decision, because of debt and other financial concerns.
Numerous surveys show that millennials find the underwriting process confusing and long, and they feel overwhelmed by the amount of information online. Therefore, it’s incumbent upon the industry to find innovative ways to improve the speed, ease and transparency of the buying process to meet increased market demand among an increasingly influential demographic.
In time, however, more millennials may consider life insurance to protect their families from future unknowns.
What is known today is that a few insurers are providing wearable devices to policyholders to secure a better understanding of customer wellness. In exchange, customers can receive premium discounts for staying healthy.
These more interactive and engaging approaches are a great example of how insurers can leverage emerging technologies to reshape underwriting and make policies more affordable – and appealing – to a segment of the population sensitive to pricing.
IT Services for Machines
AI and machine learning (ML) technologies in insurance have primarily focused thus far on human interaction. But what about machine-to-machine communications?
For years, systems have been built on design-thinking methodologies with humans as the end users. That is changing, as insurers look to address the talent gap by building systems for machines as end users.
Consider, for example, a carrier that has insured a wind turbine farm. The insurer will provide enhanced services to the insured to process weather data, local wind conditions and other aspects that affect wind power generation. It will also build a system to support machine-to-machine communication, as opposed to human interaction.
Consequently, the weather data will be sent to a wind turbine pitch system, which is designed to communicate with the rotor blades. This requires design-thinking approaches that are quite different from approaches focused on human use; to complicate matters further, this system would reside in the field and not in a central cloud environment.
In this scenario, the AI algorithms running on machines close to the edge would be making decisions autonomously. These decisions, moreover, would need to be explainable, auditable and compliant with regulations. Designing these systems will require new approaches and architectures.