Artificial Intelligence: Implications for Social Inflation and Insurance

• Author: Kevin H. Kelley,Mark Heintzman,Lisa M. Fontanetta,Nikki Pereira
• Published date: 01 December 2018
• Date: 01 December 2018
• DOI: http://doi.org/10.1111/rmir.12111

Risk Management and Insurance Review

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Risk Management and Insurance Review, 2018, Vol.21, No. 3, 373-387

DOI: 10.1111/rmir.12111

INVITED ARTICLE

ARTIFICIAL INTELLIGENCE:IMPLICATIONS FOR SOCIAL

INFLATION AND INSURANCE

Kevin H. Kelley

Lisa M. Fontanetta

Mark Heintzman

Nikki Pereira

ABSTRACT

Artificial intelligence (AI) has the ability to enhance the insurance industry’s

value chain by altering relationships, reinventing business platforms, and ex-

panding hidden data. Insurance companies will apply AI to greatly enhance

large data analytics, evolve algorithms with transactional data faster, and com-

bine data in new ways to discover better underwriting risks and appropriately

price the risk of various insureds based on the true value of their business risks.

This article explores how AI will have a significant impact on the workforce,

jobs, and furthermore how the elimination of jobs will potentially exacerbate

social equality gaps on a global scale, leading to a shift in culture and increased

social inflation, thus impacting the insurance industry as well as its customers.

OVERVIEW OF ARTIFICIAL INTELLIGENCE AND SOCIAL INFLATION

Artificial intelligence (AI) can be defined as a computer system that can sense its envi-

ronment, comprehend, learn, and take action from what it is learning. AI has also been

conceptualized as a machine that has been programmed to mimic human characteristics

while having the ability to perform tasks more efficiently and effectively than humanly

possible. Forms of AI in use today include digital assistants, chatbots, and machine-

learning technology. The spectrum of automation varies with human involvement in

the process ranging from some to almost no involvement. The systems intelligence

ranges from hardwired, which does not learn from its interactions, through to adaptive

systems, which adapt to different situations and can act autonomously without hu-

man assistance (Jubraj et al., 2018). The Skymind (2018) platform notes, “You can think

of deep learning, machine learning and artificial intelligence as a set of Russian dolls

nested within each other, beginning with the smallest and working out. Deep learning

is a subset of machine learning, and machine learning is a subset of AI, which is an

umbrella term for any computer program that does something smart” (see Figure 1).

Kevin H. Kelley is the Chairman, Board of Overseers at the St. John’s University School of

Risk Management, Insurance, and Actuarial Science; e-mail: kevin.kelley@ironshore.com. Lisa

M. Fontanetta, Mark Heintzman, and Nikki Pereira worked with Kevin as coauthors and inde-

pendent researchers.

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