I believe insurtech is more collaborative than disruptive. There are many ways insurance technology can streamline and improve current processes with digital transformation. Cognitive computing, a technology that is designed to mimic human intelligence, will have an immense impact.
An IBM Institute for Business Value survey revealed that 90% of outperforming insurers say they believe cognitive technologies will have a big effect on their revenue models.
The ability of cognitive technologies, including artificial intelligence, to handle structured and unstructured data in meaningful ways will create entirely new business processes and operations.
Cognitive computing technologies aim to promote – not hamper – progress. However, strategies for assimilating these new “employees” into operations will be essential to their success.
Leveraging Big Data to Solve Insurtech Challenges
Managing the flood of data is a major challenge.
Using all sorts of data in new, creative ways underlies insurtech. Big data is enormous and growing in bulk every day. Wearables, for instance, are providing health insurers with valuable data.
Insurers will need to adopt best practices to use data for quoting individual and group policies, setting premiums, reducing fraud, and targeting key markets.
Recent data shows 7 out of 10 consumers would share essential data on their health, exercise, and driving habits in exchange for lower prices from their insurers, an increase of 19% from two years ago. The study also found that 66% of consumers would also share important data for personalized services to prevent injury and loss—up 54% from 2020.
Innovative ways to use data are already transforming the way carriers are doing business. One example is how blocks of group insurance businesses are rated. Normally, census data for each employee group must be imported by the insurer to rate and quote, but that’s changing. Now, groups of clients can be blocked together based on shared business factors and then rated and quoted by the experience of the group for a more accurate and flexible rating.
Additionally, insurers leveraging vast amounts of consumer data for accelerated underwriting have an opportunity to obtain significant benefits. A survey by LIMRA found that 74% of insurance companies say accelerated underwriting has reduced wait times for policies, 59% say it has diminished policy issue costs, and 37% say it has helped increase sales.
A major hurdle to deploying cognitive computing across an insurance organization is security and data privacy. Only 32% of consumers say they trust insurers to look after their data, decreasing 40% from 2019.
Carriers can mitigate the risk of data breaches by investing in early detection, enforcing a culture of cybersecurity, stress testing defences, and learning and evolving from past attacks (either experienced by the carrier itself, or other carriers in the industry).
Enabling Cognitive Computing
Cognitive computing, an approach designed to mimic human intelligence, makes big data manageable.
With cognitive computing, systems require time to build their capacity to handle scenarios and situations. In essence, systems will have to evolve through learning to a level of intelligence that will support more complex business functions.
Already, chatbots like Alegeus’s “Emma,” a virtual assistant that can answer questions about FSAs, HSAs, and HRAs, and USAA’s “Nina” are at work helping policyholders. By 2026, chatbots will occupy 40% of all overall deployment within the insurance industry’s customer service roles.
Carriers can also integrate consumer data with predictive analytics to write more insurance products faster. In fact, according to recent data from Willis Towers Watson, 60% of insurers reported an increase in sales due to predictive analytics, and 67% reported a reduction in expenses.
Insurers will be challenged to create explainable, ethical systems that comply with relevant regulations.
Despite the challenges, 79% of insurance executives believe collaboration between humans and machines will be critical to innovation in the future, and 21% say they are preparing their workforce for collaborative, interactive, and explainable AI-based systems.
Evolving Data Standards
Establishing practical data exchange standards also remains a big challenge. Data exchange standards should encompass data aggregation, format and translation, and frequency of delivery.
Having a stable, backward-compatible web-service API becomes even more critical given the lack of an established and adequate data-exchange standard with third-party information providers. Data-exchange standards should encompass data aggregation, format and translation, and frequency of delivery.
Without standards, chaos can develop, and costs can ratchet up. Although there has been traction in the property and casualty industry with ACORD standards, data exchange standards for group insurance have not become universal.
While this remains a challenge, industry groups such as LIMRA and CLIEDIS are making serious inroads into developing common data exchange standards.
The Road to Insurtech Nirvana
With 25% of the insurance industry projected to be automated by AI and machine learning techniques by 2025, the future is bright for insurers that can tap large data sets and train cognitive computing models to automate tasks and deliver a superior customer experience.