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4 Ways: How Artificial Intelligence is Transforming the Insurance Industry?

Before the advent of AI, things were more manual and labour-intensive in this industry and claim processes took months to finish.Product recommendations used to be from advisor experience, too many loopholes in the insurance fraud detection system and the risk groups were classified using only static data like medical history and age.

We now have product recommendations from deep analytics of customer data, claims are processed faster using automated image classification, risk group classified using a broad spectrum of data, like clickstreams, social media, and web analytics, increased revenue, frauds in insurance more accurately detected and customer churns predicted and intercepted in real time.

Given inputs such as gender, age, state, postal code, household size, the number of dependents and annual income, an AI platform can readily recommend term coverage and burial coverage in addition to general life insurance coverage.

With big data and analytics, insurance companies can extract the intelligence that has the capability to enable improved customer service, better campaigns to prospects and customers, more effective up- and cross-selling, and increased revenues.

Machines will soon be able to devise solutions to large-scale problems in countless fields including medicine, finance, science and education thus augmenting human potential and enabling us to go further, farther and faster.

How will Artificial Intelligence transform the insurance industry?

Technology based on Artificial Intelligence (AI) has been helping transform a diverse set of industries, withlarge conglomerates like Apple, Google, Samsung, Fidelity, RBS, and IBM having alreadyshown interest in it.

Not surprisingly, the insurance industry also sees signs of promise, with AIG investing in Human Condition Safety, a workplace wearables startup that uses employee data to help prevent injury.As mobile app developers with a constant eye on wider tech trends, we believe this is just the start;

Advances in search, data management, process automation and more mean insurers can now use AI for a wealth of revenue-booting activities, from discovering new product lines across diverse geographies to identifying customer segments throughout the entire world.

AI can take customer experience to previously unimaginable levels, aided by the likes of virtual assistants powered with speech recognition training and NLP (Neuro-Linguistic Programming), making it easier for companies to provide customised products and services.

Insurance 2030—The impact of AI on the future of insurance

Scott’s personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium.

The pace of change will also accelerate as brokers, consumers, financial intermediaries, insurers, and suppliers become more adept at using advanced technologies to enhance decision making and productivity, lower costs, and optimize the customer experience.

The penetration of existing devices (such as cars, fitness trackers, home assistants, smartphones, and smart watches) will continue to increase rapidly, joined by new, growing categories such as clothing, eyewear, home appliances, medical devices, and shoes.

The resulting avalanche of new data created by these devices will allow carriers to understand their clients more deeply, resulting in new product categories, more personalized pricing, and increasingly real-time service delivery.

By 2030, the proportion of autonomous vehicles on the road could exceed 25 percent, having grown from 10 percent just four years earlier.2 2.Deep shift: Technology tipping points and societal impact, World Economic Forum, September 2015, weforum.org.

Carriers will need to understand how the increasing presence of robotics in everyday life and across industries will shift risk pools, change customer expectations, and enable new products and channels.

These cognitive technologies, which are loosely based on the human brain’s ability to learn through decomposition and inference, will become the standard approach for processing the incredibly large and complex data streams that will be generated by “active”

With the increased commercialization of these types of technologies, carriers will have access to models that are constantly learning and adapting to the world around them—enabling new product categories and engagement techniques while responding to shifts in underlying risks or behaviors in real time.

Enough information is known about individual behavior, with AI algorithms creating risk profiles, so that cycle times for completing the purchase of an auto, commercial, or life policy will be reduced to minutes or even seconds.

Auto and home carriers have enabled instant quotes for some time but will continue to refine their ability to issue policies immediately to a wider range of customers as telematics and in-home Internet of Things (IoT) devices proliferate and pricing algorithms mature.

Furthermore, products are disaggregated substantially into microcoverage elements (for example, phone battery insurance, flight delay insurance, different coverage for a washer and dryer within the home) that consumers can customize to their particular needs, with the ability to instantaneously compare prices from various carriers for their individualized baskets of insurance products.

UBI becomes the norm as physical assets are shared across multiple parties, with a pay-by-mile or pay-by-ride model for car sharing and pay-by-stay insurance for home-sharing services, such as Airbnb.3 3.Some insurtech companies are already beginning to design these types of products;

The agent of the future can sell nearly all types of coverage and adds value by helping clients manage their portfolios of coverage across experiences, health, life, mobility, personal property, and residential.

These tools help agents to support a substantially larger client base while making customer interactions (a mix of in-person, virtual, and digital) shorter and more meaningful, given that each interaction will be tailored to the exact current and future needs of each individual client.

These information sources enable insurers to make ex ante decisions regarding underwriting and pricing, enabling proactive outreach with a bindable quote for a product bundle tailored to the buyer’s risk profile and coverage needs.

Regulators review AI-enabled, machine learning–based models, a task that requires a transparent method for determining traceability of a score (similar to the rating factor derivations used today with regression-based coefficients).

Public policy considerations limit access to certain sensitive and predictive data (such as health and genetic information) that would decrease underwriting and pricing flexibility and increase antiselection risk in some segments.

Claims processing in 2030 remains a primary function of carriers, but head count associated with claims is reduced by 70 to 90 percent compared with 2018 levels.4 4.This shift to a more automated claims function has already begun.

Claims for personal lines and small-business insurance are largely automated, enabling carriers to achieve straight-through-processing rates of more than 90 percent and dramatically reducing claims processing times from days to hours or minutes.

Automated customer service apps handle most policyholder interactions through voice and text, directly following self-learning scripts that interface with the claims, fraud, medical service, policy, and repair systems.

Human claims management focuses on a few areas: complex and unusual claims, contested claims where human interaction and negotiation are empowered by analytics and data-driven insights, claims linked to systemic issues and risks created by new technology (for example, hackers infiltrate critical IoT systems), and random manual reviews of claims to ensure sufficient oversight of algorithmic decision making.

For large-scale catastrophe claims, insurers monitor homes and vehicles in real time using integrated IoT, telematics, and mobile phone data, assuming mobile phone service and power haven’t been disrupted in the area.

Insurers should develop a perspective on areas they want to invest in to meet or beat the market and what strategic approach—for example, forming a new entity or building in-house strategic capabilities—is best suited for their organization.

Most important, a detailed schedule of milestones and checkpoints is essential to allow the organization to determine, on a regular basis, how the plan should be modified to address any shifts in the evolution of AI technologies and significant changes or disruptions within the industry.

Auto accidents will be reduced through autonomous vehicle usage, in-home flooding will be prevented by IoT devices, buildings will be reprinted after a natural disaster, and lives will be saved and extended by improved healthcare.

Winning carriers of the future will create and enact strategic plans that position their brand, products, customer interactions, and technology successfully to take advantage of the new economic structure on the horizon.

The next generation of successful frontline insurance workers will be in increasingly high demand and must possess a unique mix of being technologically adept, creative, and willing to work at something that will not be a static process but rather a mix of semiautomated and machine- supported tasks that continually evolve.

As a last component of developing the new workforce, organizations will identify external resources and partners to augment in-house capabilities that will help carriers secure the needed support for business evolution and execution.

The winners in AI-based insurance will be carriers that use new technologies to create innovative products, harness cognitive learning insights from new data sources, streamline processes and lower costs, and exceed customer expectations for individualization and dynamic adaptation.

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