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5 Challenges When Innovating With AI

Artificial intelligence is booming in insurance. In a recent report, Celent identified AI use cases around the globe and across the insurance value chain.

Uses include customer engagement (USAA’s Nina); product optimization (Celina Insurance Group, Protektr); marketing and sales (Usecover, Insurify, Optimal Global Health, Ping An); underwriting (ZestFinance, SynerScope, Intellect SEEC, Swiss Re); claims (Tractable, Ant Financial, Gaffey Healthcare); fraud detection (Ant Financial, USAA); risk management (Achemea); and business operations (Ping An Direct, Union Life).

Insurers are wise to innovate with AI technologies. Early adopters will face challenges but will also have the potential to reap greater rewards by improving efficiency and customer engagement.

Here are five challenges for carriers to consider when innovating with AI:

1. What technology to use when. When embarking on a digital transformation, there may be a number of solutions available for a given problem, one of which could be AI. But while AI may resolve an issue, it is important to examine all the potential solutions and decide which one is the best fit. Perhaps robotic process automation (RPA), application programming interface (API) or another automated solution is best suited. Can an existing technology be leveraged?

Deciding what solution to apply when requires you to look at the whole organization and all the issues upfront. This allows CIOs and CEOs to examine each problem, decide on the right technology solution and make sure it complements the overall strategy and budget.

See also: Strategist’s Guide to Artificial Intelligence  

2. Big data + AI = big strategy. A second challenge surrounds the management of big data obtained from customers, core systems, brokers/agents and insurance exchanges. Add to that the varied types of data that AI is managing, analyzing, communicating and learning from and things get a little more complicated. Here’s a list of the different data types AI may be working with:

  • Structured, semi-structured and unstructured data
  • Text
  • Voice
  • Video
  • Images
  • Sensors (IoT)
  • Augmented/virtual reality

Data is also classified as real-time, historical or third-party — yet another dimension to consider. Make sure your strategy takes the necessary data variables into account: where data will come from, where it will flow to and how it will be handled at various points in the customer journey.

3. Managing customers across swim lanes. This leads us to challenge No. 3: the ability of AI to engage with customer data at key touchpoints during the customer lifecycle. For example, if Lucy has group benefits as well as voluntary products, car and house insurance, how will her data be managed and optimized across swim lanes?

What will be the touch points for AI? When will other insurtech solutions be present? When is human intervention required? And how will this data be used to inform future risk decisions?

4. Harnessing AI’s multidisciplinary capabilities. AI encompasses machine learning, deep learning, natural-language processing, robotics and cognitive computing, to name a few. You can read my blog post here to learn more. Deciding what technical abilities will be required to solve your problem could present challenges as the lines between disciplines blur.

Additionally, the next wave of AI could come from entirely different industries, such as aerospace, environmental science or health — but  it will still have applications for insurance. The best way to overcome this is to examine your AI needs across solutions and select vendors with the right capabilities to execute them.

See also: The Insurer of the Future – Part 3  

5. Communicating past tech speak. As AI becomes mainstream, the challenge of helping non-technical business professionals understand these complex applications is real. AI systems can require a level of technical expertise beyond the everyday scope of business.

True digital transformation, regardless of technical complexity, affects everyone in the organization. Ensuring the vision is shared will matter as day to-day operations, tasks and activities change. Find someone who can break down the benefits of these new solutions into bite-sized pieces that everyone understands to ensure buy-in and ultimate success.

The question of whether AI will indeed disrupt the industry or simply enable its full digitization is still not known. It will not be the solution to every problem. However, if implemented strategically, it may hold the capacity to create an entirely new way of insuring — and delighting — customers in a rapidly changing landscape.

Robots and AI—It’s Just the Beginning

You’ve probably had regular help from a virtual assistant by phone or online, assisting you with basic tasks such as directing your call or giving you your bank balance. Helpful, right? The companies that employ the virtual assistants think so, too, and are applying these AI/robotic processes to more and more of their everyday business operations.

Often called out for being slow to change, the insurance industry is beginning to catch up quickly. It’s making sweeping changes across organizations and core systems because of the disruptive emergence of insurtech. Carriers like Celina and USAA are using AI in their daily operations and reaping the benefits.

As a result, insurers are now either delivering — or are in the process of delivering — a great digital experience to consumers. Once complete, this transformation will entail an entirely new way of doing business and servicing customers.

See also: Strategist’s Guide to Artificial Intelligence

There are four main technologies to keep in mind:

Robots

Robotics is the branch of technology that deals with the design, construction, operation and application of robots, virtual or physical. They are autonomous or semi-autonomous machines or systems that can act independently.

Artificial Intelligence

AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and translation. AI is software that learns and improves. Some robots can use AI to improve their capability by learning, but that is optional.

Cognitive Computing

Cognitive computing technologies are a subset of AI. Cognitive computing “refers to computing that is focused on reasoning and understanding at a higher level, often in a manner that is analogous to human cognition,” writes Lynne Parker, director of the division of information and intelligent systems for the National Science Foundation, in Computerworld. “This is a subset of AI that deals with cognitive behaviors we associate with ‘thinking’ as opposed to perception and motor control.”

Robotic Process Automation

Insurtech consultant Celent defines robotic process automation (RPA) as a set of technologies that can automate processes that currently require human involvement. Robots replicate human behavior to conduct the tasks as a human would; robots also optimize the tasks. RPA can yield benefits when applied to the right roles. It does well supporting repetitive tasks in various environments where there is little change, often back-office support roles and tasks.

Accenture found that cost savings after deploying RPA can reach as high as 80% and time saved on tasks as high as 90%. Automating repetitive processes means tasks are completed quickly with fewer errors, opening up new opportunities for employees to focus on more customer-centric tasks.

But RPA is not the answer to everything. It does not think, reason or predict. It completes simple, repetitive tasks quickly, but it does not learn or self-improve. Developing an enterprise-wide strategy to determine where RPA provides the most value and to anticipate the organizational change that may result is the prudent approach.

The Future Is Here

IBM’s Watson and Amazon’s Alexa are early examples. Insurers already have joined the revolution. Celina Insurance Group uses an analytics-based agency prospecting tool to appoint agents in high-potential underserved areas. USAA’s “Nina” is an AI virtual assistant that chats with customers on the USAA website. It’s designed to respond to 120 questions, from reporting stolen payment cards to changing a PIN.

See also: The Big Lesson From Amazon-Whole Foods  

There will inevitably be lessons to learn from successes and failures of this first wave of robotics and AI. However, early adopters of these technologies also risk success. Investing in innovation is what will allow insurers to stay ahead of disruption and, in some cases, create it.

As robots evolve, their capabilities and applications will no doubt be vast. Just as we could not have predicted how the internet — and now the Internet of Things — would evolve, robotics and artificial intelligence will likely follow the same course.