The Great AI Race in Insurance Innovation

//The Great AI Race in Insurance Innovation

The Great AI Race in Insurance Innovation

ai_insurance

The rise of artificial intelligence is the great story of our time. Leaving the laboratory after decades in the making, artificial intelligence, or AI, is infusing itself into many aspects our daily lives – from homes and phones to cars and offices. Machines are now able to perform tasks that previously required human intelligence across various industries. Insurance, once perceived as highly resistant to change, has now accelerated the race for innovation.

Placing AI at the forefront of the innovation agenda, insurers have been separating the hype from reality to reinvent business models. Insurance has accepted the fact that AI isn`t coming — it’s here. Companies are racing to apply artificial intelligence to find a 10X improvement.

The following case studies provide a first-hand look at how today’s pioneering insurers are advancing strategic growth and transformation with artificial intelligence:

AI in Consumer Engagement

Insurers are constantly seeking opportunities to enhance the trust and relationship with customers, as the industry has always suffered from a lack of frequent and direct engagement. Today, AI is increasing being applied to collect large volumes of real-time data at very high velocity, recognize patterns of customer behavior and engage in deeper interactions for a more personalized and engaging overall experience with customers.

As AI is vying to become an indispensable part of customers’ everyday life, intelligent personal virtual assistants like Amazon’s Alexa, Microsofts’s Cortana, Google’s Now, Facebook’s M and Apple’s Siri are evolving to learn customers’ preferences and behavioral patterns and then making recommendations and potentially acting on behalf of the customer. Using just voice services, customers are now able to interact with insurers through a more intuitive channel, from asking everyday insurance questions to getting an insurance quote, or simply navigating the insurance process.

AI bots’ are becoming the new user experience (UX). Chatbot technologies are engaging customers on websites, mobile apps and messaging services such as WhatsApp, Facebook Messenger and SMS using natural language. The advancements in conversational AI agents, including their ability to adapt to speech patterns, vocabulary and personal preferences, have driven insurers to take things to the next level with full conversational interactions powered by AI bots throughout the customer journey. From a customer perspective, it`s truly a game-changing experience as we could now simply ask a question through speech or text and have insurers resolve problems or attend to an inquiry, at any point in time from any digital interfaces (including websites and mobiles apps) instead of navigating our way around complicated websites or time-consuming contact centers. Some insurers have successfully launched Alexa-integration, allowing customers to quickly access important information such as policy premium status, as well as make payments and recommend additional coverage based on lifestyle changes.

Although these advancements won`t be able to replace an agent in the short term, AI agents are learning at unprecedented speed, and this is just scratching the surface of what’s coming. A recent Gartner study predicts that, by 2020, the customer will manage 85% of its relationship with an organization without human interaction. While we know analyst projections may at times be over-optimistic, the reality is that AI likely will be the basis for competing on customer experience from here onward. There’s no turning back.

AI in Automated Advisory

Some insurers will leapfrog the innovation race with automated insurance advisory. With robo-advisers, insurers can now offer real advice without the need for any human intermediaries, anytime and anywhere.

The complexity of insurance often frustrates customers and leads to mistrust. It is also hard to decouple decisions from emotional and social reasons or agent bias.

Robo-advisers can build a consolidated financial portfolio, often aggregating data from various insurers and financial providers including life and health coverage, annuity accounts, savings, brokerages, etc. Robo-advisers then combine behavioral and external data to simulate future risk preferences, running future scenarios to infer cradle-to-grave financial plans and investment management advice.

AI in Underwriting and Claims Management

Increased automation in claims management and underwriting holds the promise of delivering a more customer-centric experience.

Today, AI-based agents are building predictive models for processing and settlement of claims expenses and high-value losses with far lower costs and heighten levels of efficiency. Tasks that took typically months are now accurately achieved in a matter of minutes, allowing insurers to focus on value-added activities. In early 2017, tongues started wagging when Lemonade used AI to settle a claim in three seconds and Fukoku Life of Japan displaced 34 employees with IBM’s Watson Explorer AI, for a 30% productivity increase.

Software developed using machine learning gathers all the details that underwriters need, while also identifying hidden risks.

Insurers are racing to routinize more work with artificial intelligence automation in core insurance business process areas such as fraud detection, policy services and contract administration, claims administration and risk compliance. We foresee increased application of artificial intelligence in any task that’s high-volume and highly repetitive and demands low human judgment, reaping sizable costs savings.

AI in Pricing Risk

Traditionally, insurers use generalized linear models (GLM), with predefined variables such as age, sex, location and occupation class, then fitted with additional factors/variables for predictions.

Today, modern machine learning techniques have increased speed, sophistication and accuracy, accelerating the adopting of usage-based and behavior-based pricing. Motor, alone, has seen a constant stream of telematics data ingested into machine learning models; driving patterns are not only used for accurate pricing of risk but also to prevent accidents by alerting drivers with behavior tips and with information about traffic and road conditions.

Health insurers are capitalizing on wearable technologies such as Fit Bits and Jawbone to drive individuals toward better health. By linking incentives to customers with healthy lifestyle characteristics such as regular exercise, walking, running, cycling, swimming and a healthy body mass index (BMI), insurers are lowering risk — and premiums.

With shorter modeling response time, increased actuarial simulation and the capability to learn, machine-based pricing is marching toward becoming an industry standard much quicker than we anticipated.

The Future

The work in artificial intelligence is just beginning. Insurers are aggressively exploring opportunities.

Winners will be determined by the velocity and scale of their use of AI and by the ability to go beyond pure business results. After all, the fundamental promise of an insurer is to help customers live their lives with peace of mind — healthier and safer.

Note:  This was first published on InsuranceThoughtLeadership.com

By | 2017-09-08T22:00:14+08:00 November 3rd, 2014|Categories: AI Blog|1 Comment

About the Author:

Being an Innovation Evangelist, Harphajan is a well-established expert in leveraging Artificial Intelligence and Data Science

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