In 2017 the US insurance industry recorded $1.2 trillion in “written premium.” The premiums are split 52%/48% between Life and Annuity companies and Property and Casualty companies. Health insurers are not included in these totals. At $1.2T, insurance contributes about $602.7 billion to GDP (about 3.1%). To put this in perspective, this places insurance just below the federal government contribution to GDP and just above the information technology sector. The industry as a whole has cash and invested assets over $5.5 trillion and employs 2.5 million people. Worldwide, US insurance business represents about a third of the total, split more or less evenly with Europe and Asia.
Insurance is a complex industry, with many layers, disciplines, markets, and regulators. It is, however, more reliant on data and analytics than most industries, with a few exceptions, but has struggled to keep up. Of all of the industries highlighted in case studies and sales pitches for big data analytics and AI/Machine Learning, insurance is conspicuous by its absence. It is not only desperately in need of digital transformation, but it can also benefit from a wide assortment of technologies and applications.
Insurance companies need to implement a variety of digital technologies including big data analytics, IoT, machine learning and AI. Some of the subject areas that are relevant include:
- Back office automation
- Personalized user experience
- Voice biometrics
Back office automation - Insurance companies have more office workers than other industries that keep outdated processes flowing. The application of automated workflow and decision-making can reduce costs, improve performance and lay the foundation for more sophisticated innovations, especially hastening the introduction of new products, essential in what is a very competitive market.
Personalized user experience - With the exception of commercial lines, most insurance products are sold to individuals. The process of researching and securing insurance coverage online is still quite tedious with many steps, plus terms and conditions that are not often understood. In the past decade, aggregator websites provided a search and compare service, but typically, the insured chooses an option and is funneled back to the existing underwriting and policy issuance process.
The process of reporting a claim is a complex multi-step ritual that benefits by the application of new technology. Some auto insurers are experimenting with “touchless” claims where the driver takes a picture of the damage and sends it to the company with nearly instant authorization for repairs. In some cases, the entire process is conducted without human intervention, but the technology is in the early stages.
Voice biometrics - can reduce the time-consuming process in a customer call of repeating policy information, etc. by identifying the client in the first seconds, and also sensing emotion, directing the caller to the proper responses.
Actuarial - Are actuaries trained to handle 21st-century data and tools? There are at least 25,000 actuaries employed in the US, each with academic, professional and work experience that is mathematical, quantitative and ready to leverage the benefits of big data analytics including machine learning and AI. They will need the training to acquire new skills, but they represent a pool of professionals with the temperament and background to add data science to their work. Actuarial departments are, however, still reliant on manual data preparation and coding in low-scale languages and tools, especially desktop tools.
IoT - Some providers of home-owner’s insurance have already participated with third-party providers of “smart homes” using the sensor streams to spot events, and improve security. Insurers of other physical assets such as structures, vehicles and even ships in port and at sea are using drones and satellites to absorb telemetry for risk analysis and aversion. Personal lines insurers are using drones to inspect properties during the underwriting process.
Telematics - Telematics is one area where big data analytics are showcased with insurers, particularly with personal auto insurance and fleet management. The insurer (or a third party data aggregator) supplies the driver with a device that plugs directly into the vehicles OBD II port (all cars and light trucks built and sold in the United States after January 1, 1996, were required to be OBD II equipped). In principle, the device records the driving habits of the vehicle, rewarding good drivers, though it is unlikely that is all it does. Surely, penalizing drivers for poor habits is also part of the program as is aggregating the data and selling it to third parties. Some State Insurance Commissioners (in the US, insurance is regulated by the states) are evaluating whether these practices are acceptable. Holding and using big data about people and lives is a privilege. Access to this data for legitimate underwriting and claims adjudication purposes is reasonable, but abusing the privilege needs to be avoided with careful governance.
The blockchain - Blockchain can support innovative business processes and be the foundation for new products. One example is peer-to-peer insurance by hosting quoting, claims and other tasks. Blockchain also provides transparency, accuracy and currency of contracts to all parties in a contract. Faster and secure payment models and enhanced security reduce fraud and risk of duplication.
My back of packet estimate is that most current back-office work processes will disappear in the next 5-7 years, of necessity, as the burden of manual processes on costs (and product pricing) and product innovation latency will be too much to bear. On the other hand, staffing of actuaries and actuaries performing as data scientists will likely increase 25% in the same period.
With the exception of some large insurers, the industry as a whole has lagged behind others in exploiting big data analytics, machine learning, and AI. There are a multitude of benefits for insurers to embrace digital transformation in every aspect of their business, and there is great risk if they do not because as a business, insurance is almost completely driven by the processing and analysis of data.
One can only generalize about insurance companies because there are so many types and so many models of operation. However, most need to revise their processes to reduce cost and add agility in product development and cadence. Insurance companies often get poor grades for customer engagement, an area that is ready for great leaps in improvement with the application of big data analytics, machine learning and AI.