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Intellect SEEC’s Pranav Pasricha

Pranav Pasricha, CEO of Intellect SEEC, discusses the company’s goal of helping insurers keep the insured top of mind in their innovation efforts, and maintaining that view throughout the evaluation, development and integration of new technologies.


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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.

Advanced Telematics and AI

Solution providers are quickly advancing innovative approaches to the next generation of emerging technologies that are affecting both insurers and policyholders. SMA has acquired a unique historical view of how solution providers are innovating within insurance through our annual SMA Innovation in Action Awards – and we have seen how it has changed over the six years of the program. The two winners of this year’s SMA Innovation in Action Awards for Solution Providers are excellent examples of how the use of technology like telematics and artificial intelligence (AI) is maturing in the insurance industry.

TrueMotion’s smartphone usage-based insurance (UBI) product combines mobile technology, machine learning and data science to help insurers better understand and mitigate risk, acquire more profitable customers and increase customer loyalty as well as giving individual users feedback on their driving habits. TrueMotion’s solution illustrates how solution providers are expanding the possibilities of existing technologies. Telematics is firmly established as an insurance technology and continues to spread from personal to commercial lines of business. TrueMotion’s innovation lies in the refinements it has made and how it is using telematics, sensors and mobile technology to effect measurable improvements to drivers’ behavior. Not only does TrueMotion use the sensors in a user’s own smartphone in place of an on-board device (OBD), the company can determine when a driver is actually behind the wheel based on the position of his phone. This solves a weakness in OBD-based telematics: how to link good or bad driving habits to a specific driver rather than a specific vehicle, while still excluding trips that driver makes as a passenger.

See also: Strategist’s Guide to Artificial Intelligence  

The sophistication in data collection and the advanced analytics on the back end allow TrueMotion to give drivers immediate feedback for more effective behavioral modification. TrueMotion also takes an innovative approach in monitoring how the user interacts with her phone while driving, allowing for real-time alerts if distracted driving reaches dangerous levels.

Solution providers are also moving beyond emerging technologies in the physical domain, like connected vehicles and drones, to those of the virtual realm, like artificial intelligence, cognitive computing and blockchain. These technologies have been slower to gain a foothold in the insurance world, partly due to a slower development timeline and because their insurance applications were still to be determined. That is already changing. Today, interest in artificial intelligence and machine learning, in particular, has skyrocketed.

Intellect SEEC’s Intellect Risk Analyst is an AI-based risk discovery and assessment software for the commercial insurance industry. It aggregates data from more than,800 structured and unstructured data sources and applies rules set up by the underwriter. Machine learning analyzes the resulting underwriting decisions and refines its data aggregation and analysis capabilities in response.

This solution gives insurers an accessible way to apply big data and AI to their existing transactional processing – in this case, underwriting. Integrating big data with advanced AI technologies gives underwriters access to new information and insights on the proposed risk. These new insights for risk assessment and pricing, together with the continuous improvement of data aggregation with machine learning, are powerful assets for underwriting.

See also: It’s Rush Hour in Telematics Market  

In these continually changing times, it becomes critical for all members of the insurance ecosystem to collaborate on new ways of doing business. Technologies that are focused on improving risk and harnessing the power of AI and machine learning, for example, showcase how resources and technology are interacting to change the insurance landscape.

Innovation is flourishing across the insurance industry, and there are still great opportunities ahead. As solution providers continue to grow their use of advanced technologies into new territory, the potential value of their partnership increases for insurers. We expect to see solution providers’ innovations play a key role in the transformation of the insurance ecosystem.