(Image credit: AdobeStock.)
While artificial intelligence (AI) is already in use across many industries, it is projected to grow exponentially in future. So called “weak” AI—such as today’s chatbots—in coming decades will give way to more human-like “strong” AI, which will probably disrupt current business models. How will insurance be affected? How can insurers prepare?
Current AI Trends
Artificial intelligence (AI), or machine learning, is software that can think and learn like a human. Today, basic forms of AI are able to perform specific tasks—like checking an insurance claim for fraud—but future generations will be capable of solving complex problems and making decisions.
AI promises to improve productivity through automating simple tasks and offering new insights from analyzing data and is expected to boost national economies worldwide by more than 30 percent by 2035, according to Accenture. However, for businesses, potential AI threats could easily counterbalance the huge benefits, as they face new liability scenarios and challenges as responsibility shifts from humans to machines.
Already, AI is used in almost every industry: from financial assistance chatbots to driverless cars and from the eradication of certain incurable diseases and delivery of healthcare to remote areas to software that better predicts the weather and tracks the effects of climate change with smart technology and sensors that help reduce emissions.
“Weak” and “Strong” AI
AI “smartness” is proportional to the amount of learned data and not based on high-level semantic concepts such as “risk”, “competition”, “pay-back”, “goal”, “fairness”, etc. Such a difference between how AI and humans learn and conceptualize the world is what differentiates current from future AI systems—respectively “weak” AI and “strong” AI.
“Weak” AI refers to AI technology currently deployed in business. In contrast, “strong” AI, which will approach human cognitive abilities, are theoretical and not expected on the market until around 2040. The introduction of “strong” AI agents will most likely disrupt society as we know it. One of the sectors which AI has already influenced and which it will increasingly disrupt in future is insurance.
Transforming the Corporate Insurance Sector
Insurance, which uses lots of data and repetitive processes has been an early adopter of machine learning, partnering with technology firms and investing in start-ups on many applications. Tata Consultancy Services’ Global Trend Study found that of 13 industries surveyed, insurance invested $124 million in AI, almost double the $70 million cross-sector average.
To date, insurers have mainly focused on developing AI applications for personal lines while those in the life and health market are already using AI to review and analyze policy wordings and validate claims. Last year, start-up insurer Lemonade claimed a new world record saying it used an AI claims system to accept and pay a claim for a lost item of clothing within three seconds, and without any paperwork.
But, increasingly, insurers are beginning to analyze how AI will impact commercial insurance, including the large corporate market where such products as motor and workers’ compensation lend themselves to automation.
AI is likely to impact insurance in three key areas:
- automation of insurance processes, such as claims and underwriting;
- improved understanding of business risks; and
- increased direct interaction with customers.
AI has the potential to bring about significant cost savings, as well as to speed-up the insurance transaction process. It should enhance services like analyzing submissions, checking or verifying policy documents, developing new insurance solutions and flagging potentially fraudulent claims. There are many areas, such as reputation, cyber, supply chain and economic and climate risk scenarios, where machine learning could help better assess risk.
The claims process, in particular, would benefit from increased automation. AI and automation would make for a much faster and more efficient settlement for lower value claims. Even with more complex commercial claims, however, AI could support claims decisions, speed-up some processes and make for a more customized claims service.
By automating repetitive tasks, people would be free to focus on value-added work, such as client relationships, risk assessment or providing technical support. Insights gained from data and AI-powered analytics could expand the boundaries of insurability, extending existing products, as well as giving rise to new risk transfer solutions in areas such as non-damage business interruption and reputational damage.
But there are also challenges for insurers. To maximize the most benefits of AI, traditional covers such as liability, casualty, health and life insurance will need to be adapted to protect consumers and businesses alike and insurance will need to better address certain exposures such as cyber-attack, business interruption, product recall and reputational damage.
The 24/7 Insurer
Improved 24/7 customer/market analysis and advising will be the result of increased AI use, along with improved binding, servicing and claims.
Allianz has developed Allie, an online assistant available 24/7 to answer personal lines customers’ questions. It is also using machine learning to carry out risk assessments and support automated underwriting in the small- to medium-enterprise (SME) space. It has even developed a tool that uses machine learning to identify accumulations of business interruption risk in supply chains and map networks of critical suppliers across industries.
For large commercial and corporate clients, insurance needs to be bespoke, allowing for a platform approach to service. AI can help create an environment for insurers and third parties, offering a more targeted spread of risk management and insurance services.
AI could also boost data and analytics and will be the key to unlocking data, especially as more is made available by the Internet of Things (IoT). It could enable insurers to better understand customers’ risks, help businesses reduce exposures, and find solutions for perils that currently may not be insurable.
AI-powered analytics could help companies better understand their cyber risks, improve security and even defend against cyber-attacks. At the same time, AI could assist insurers in assessing and spotting accumulations of cyber exposures.
There are many areas—such as reputation, supply chain and economic and climate risk scenarios—where machine learning could help companies better understand their risks.
As the technology becomes more sophisticated, AI applications for analyzing risk will evolve. AI could act as an “intelligent agent” able to create different scenarios and outcomes and potentially make decisions. The next generation of machine learning will move from increasing risk awareness to proactive decision-making.
AI will also work alongside other technologies, most notably the IoT and blockchain, to increase our understanding of risk and enable insurers to offer new, faster and more customized services. For example, sensors on shipping containers are already providing data on the location and condition of cargo, which, once analyzed, can trigger insurance cover or mitigation measures if the goods are damaged.
Insights gained from data and AI-powered analytics could expand the boundaries of insurability, extend existing products, and give rise to new risk transfer solutions in areas like non-damage business interruption and reputational damage.
 Accenture, How AI boosts industry profits and innovation, June 21, 2017
 Müller, V. and Bostrom, N. Future progress in artificial intelligence: A survey of expert opinion, Fundamental Issues of Artificial Intelligence, 2016