How AI Transforms Risk Engineering

“AI could contribute to the global economy by 2030, more than the current output of China and India combined.”

In a year marred by crisis and uncertainty, the mature property and casualty (P&C) insurance industry has seen its workload increase in both volume and complexity. According to the Insurance Information Institute, insured losses from natural catastrophes in 2019 totaled $71 billion. That number is only expected to rise in 2020 with the onslaught of hurricanes and wildfires hammering the U.S.

Insurers must contend with a rapidly changing risk landscape. Falling interest rates, climate change, man-made risks and civil unrest are causing unprecedented destruction and business interruption. This is exacerbated by the COVID-19 pandemic, cyber security threats and global terrorism, causing the number of claims to skyrocket.

Traditional methods of risk analysis are slow and expensive. Risk engineers spend considerable time performing repetitive assessment and administrative tasks that do not add value to clients.

One saving grace is the global movement toward digital transformation and automation, including the adoption of artificial intelligence (AI). Changing client expectations have propelled organizations to rethink age-old processes. 

An artificial intelligence study by PwC said, “AI could contribute to the global economy by 2030, more than the current output of China and India combined.” The same report estimated $6.6 trillion would likely come from increased productivity alone.

See also: Stop Being Scared of Artificial Intelligence

How do you know if you’re ready to embrace AI, and what are some of the areas it could improve within risk engineering? Below are three points to consider:

The easiest way to get started, is to contemplate your market in five years’ time and consider what capabilities you will need to compete – McKinsey

1) Align Business and AI Goals.

A certain appetite and readiness for change is required on the part of the C-suite and by the risk engineers within your workforce. A real pain point must be met, and the implementation of AI must align with the overarching business goals of your organization. For risk engineering, the time is ripe for AI disruption. According to McKinsey, “Efficiency improvement is an imperative. The industry’s current trajectory is inefficient and unsustainable, creating the conditions for disruption. This would involve digital technologies, automation and data and analytics to not only reduce error-prone manual processes but also enable an agile way of working.” 

If account engineers and risk engineering consultants spent more of their time on risk verification and selection rather than aggregation and analysis, this would help underwriters speed up the time to assess and quote on a new bid and ultimately increase the chances of winning business. The first response to a submission wins over 50% of the time. 

Still, the question remains, whether your organization wants to be an early adopter, fast follower or follower. Will the AI solution you create in-house or via a third-party vendor disrupt the sector and provide you with a competitive edge?

2) Examine Internal Talent. Find Your AI Champions.

Another critical factor is talent. Are there champions within your company willing to take on the added time it requires to inform the user journey and customizations, perhaps even label the initial data and ultimately execute on the AI opportunity at hand? It is vital that there is a top-down and, equally, a bottom-up culture of adoption for AI implementation to succeed.

A global digital practice survey revealed that insurance companies are attracting less digital talent than other financial services companies such as fintech and asset management. In a recent survey, 80% of millennials said they have limited knowledge of the insurance industry, and 44% said careers in insurance sound “boring.” Orbiseed's recent interview with a veteran risk engineer also revealed that the majority of senior risk engineers are close to retirement and may resist employing new technologies. “Indeed, perception can shape reality, and the current reality is that the insurance industry isn’t viewed as relevant or exciting to up-and-coming digitally savvy workers,” the report concluded. 

3) Partner With AI Vendors You Trust to Scale Quickly.

An AI firm should know your industry inside and out, have secure networks to help protect your data and enable you to scale your AI program fast. You will also need to consider whether to select AI integrations over ground-up builds. An integration will vastly reduce the time it takes to produce a working model for your business. A good software integration will also layer into the existing system you have rather than force your employees to learn an entirely new system.

See also: 3 Tips for Increasing Customer Engagement

Next Steps Toward AI Transformation in Your Organization

AI is fundamentally changing the way business is done in 2020. For mature industries that still rely on manual, labor-intensive processes, adopting new technology can make a measurable difference in efficiency and deliver significant competitive advantages.

Risk engineering seeks to manage risk: Adopting AI practices early will ensure that your organization hedges against the risk of falling behind the competition. Firms that effectively adopt AI early report significant performance gains compared with competitors, including higher revenues and reduced expenses.

Jack Liu

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Jack Liu

Jack Liu is the CEO and co-founder of Orbiseed, a lightning-fast AI platform that standardizes and summarizes commercial property data for insurance carriers to reduce costs, improve win rates and manage risk better.

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