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automated analytics in claims images or video, customer Customers may also end up paying more for insurance
voice, claims summary reports and so on. Cloud-based because policies are not tailored for their unique needs. In
predictive services leverage deep learning to train machine an age when most of our daily activities are online, digitized
learning-models on unstructured data sources like images, and convenient, insurance is not always a happy customer
texts, videos, and voice. These pre-trained models are then experience. There would be a global push by insurance
offered to insurers in an off the shelf package. companies to augment their technological capabilities so
that they can do business faster, cheaper and more securely.
Product Gaps: A product gap is created when new In the past few years, there have been some prominent
technologies, changing lifestyles and changing business examples of insurers investing heavily in Artificial Intelligence
models create new risks or new ways solutions.
of addressing old risks. Start-ups in the
wider world of IoT (the Internet of
Things) are offering usage based
insurance (UBI) solutions such as
telematics for motor and health
insurance leveraging a wide range of
machine learning algorithms to
normalize and analyze the big data
which is generated every second. Start-
ups supporting agricultural insurance
operations use weather and crop data
collected through satellites, drones and
weather monitoring stations. ML
algorithms are used to normalize and
analyze this data.

Customer Interaction Gaps: IMAGE 3- Reasons for adopting AI.
Emerging technologies have changed customer behaviours Artificial Intelligence has shown its substance in various
and expectations. This creates a gap in customer-facing business verticals by rapidly creating controlled, digitally
insurance operations such as distribution, policy servicing, enhanced automated environments for maximum
and claim settlement. NLP based machine learning productivity. Apparently, Insurance companies, in particular,
techniques are enabling chatbots to understand customers' have a lot to gain from investing in AI-enabled technology
queries. Then, SAI based rules are employed to find that can not only automate the scheduling of executive-level
appropriate answers to their queries. SAI is enabling online tasks but can also enrich service quality by helping agents
or app-based distribution platforms to recommend the most make right decisions and irrefutable judgments. Insurance
suitable insurance products quickly by asking an intelligently- companies are striving for a technologically advanced system
ordered minimum set of questions. Machine learning-based that helps keep all their employees synchronized. These
algorithms then predict the purchase preferences of the employees vary from agents, brokers, claim investigators to
given customer and appropriately customize the insurance market and support team. These group of employees
offering. coupled with redundant processes create layers of confusion
in Insurance ecosystem. To make the system more refined
AI in Insurance: and efficient, they should opt for stable and consistent AI-
powered solutions that can penetrate the layers of confusion
Insurance is an old and highly regulated industry. Perhaps and propel clear value proposition towards customers.
because of this, insurance companies have been slower to
embrace technological change compared to other industries. How are Insurance Companies Imple-
Insurance is still steeped in manual, paper-based processes
that are slow and require human intervention. Even today, menting Artificial Intelligence (AI)?
customers are faced with time-consuming paperwork and Insurers are using AI to provide better, faster and cheaper
bureaucracy when getting a claim reimbursed or signing up services to customers. Artificial Intelligence (AI) has become
for a new insurance policy. a buzzword in the insurance industry. Still, the industry has

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