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AI and machine-learning technologies are transforming the breadth of insurance processes. How can insurance companies adapt before they are left behind?

IF DATA is the fuel of the insurance industry's digital transformation, then artificial intelligence (AI) and machine learning (ML) could be the engine. AI and ML technologies are rapidly driving change and growth for insurers who invest significant capital to build out their capabilities. But broader applications of AI and machine learning have yet to penetrate the industry. “The focus has been primarily on mitigating and closing claims,” said Mark Tainton, global head of analytics at Ventiv Technology. “In terms of the life cycle of a claim, mitigating and closing are the middle parts, which involve looking for fraud, driving straight-through processing, implementing automation, and so on.” "While the industry has spent significant money on AI technology, it has struggled to define the business-use case of AI outside of these traditional applications," Tainton continued.

Ventiv Technology is a leading global provider of risk-management information systems (RMIS), enterprise risk management (ERM), insurance claims, billing, policy, and administration technology integrated with its market-leading analytics and predictive models. With over 45 years’ experience, Ventiv serves 450 customers and more than 450,000 users across 40 countries. Ventiv’s global footprint and experienced team of industry veterans deliver insights to organizations that allow our customers to predict, manage, and respond to risk.

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“The velocity of data is increasing exponentially. Unstructured data is going to be the bigger challenge in terms of how people can architect and synthesize that data to make sense of it and take advantage”

Mark Tainton, Ventiv Technology

Ventiv Technology is a leading global provider of advanced analytics for risk, claims, and underwriting solutions. It has partnered with more than 450 organizations worldwide to transform the use of risk and insurance information. As the industry moves toward an AI-driven future, Ventiv has expanded to other fields of application. "We're looking at 'prevent and contain,' and we've started to look at things around 'evaluate and prevent.' Post-event, how can we automate the frequency of identification of claims leveraging machine learning and AI?" Tainton said. The International Data Corporation (IDC) forecasts global spending on AI technology, including software, hardware, and services, to reach US$118 billion in 2022 and surpass $300 billion in 2026. According to the IDC, organizations across many industries are now more willing to take advantage of AI systems' efficiency benefits and enhanced capabilities.

Applying AI and L to the insurance industry The disruption of AI technologies comes as the insurance industry faces a rapidly changing risk landscape. Changing weather patterns have increased the severity and frequency of natural catastrophes, representing a significant financial hazard for insurers. But sophisticated data modeling through AI can help insurers manage those risks. "At Ventiv, we have built tools that leverage state-of-the-art AI to predict scenarios. We can do simulations and predict where storms will go," said Tainton. "Simulations make risk managers more effective because they can see that a storm is coming and predict how it would affect a client's properties. If that storm's trajectory changed, how would that change the impact?"

Workers' compensation is another branch of insurance that can benefit from AI-driven analytics. "We are automating several processes around how you score an injury to a body. Through automation, we can also assume the duration of time it will take to get an individual back to work after an injury. We have those data points at our fingertips so that when an event occurs, we know what to expect, how to triage it, and how to identify whether a case is going to litigation," Tainton said. Besides boosting insurance processes, AI-driven analytics can also help the industry solve one of its most pressing problems: the talent gap. "A large proportion of the industry's aging demographic is in the adjuster community," Tainton said. "They spend roughly 40 years in the field learning their trade. How do you onboard new people and shorten that cycle [of knowledge accumulation] to 10 to 15 years?" The answer? Through AI. "Ventiv is focused on building capabilities around straight-through processing and identifying claims that could go to litigation. If you can highlight those data points to recently onboarded people, they can understand the characteristics or the attributes associated with the claim far more quickly," Tainton explained.

The data challenge The transformation that AI and ML bring is also a double-edged sword for the insurance industry. The more that cutting-edge software can sort through mammoth troves of data, the more tertiary data it creates. The compounding effect will pose the next challenge for insurance companies. "The velocity of data is increasing exponentially. Structured data is one thing, but unstructured data is going to be the bigger challenge in terms of how people can architect and synthesize that data to make sense of it and take advantage," Tainton said. Structured data is clearly defined and searchable, while unstructured data is usually stored in its native format, such as text, audio, or video files.

“If you don't invest [in AI] now, you will be left behind. Others will take your market share very, very quickly. Embrace the future because it's coming, and you can't stop it.”

Mark Tainton, Ventiv Technology

"But even though that might scare a lot of companies, don't be scared to invest in AI. Because if you don't invest now, you will be left behind. Others will take your market share very, very quickly. Embrace the future because it's coming, and you can't stop it." AI adoption has increased exponentially over the last three to four years, primarily accelerated by the COVID-19 pandemic. But the industry's uptake hasn't been standard across the board. "Smaller players are making strategic investments in purchasing AI because they need the leverage to bring on

The aging workforce may also be hampering progress. Older insurance executives may be more hesitant to embrace new and unproven software – a mindset that can be difficult to shift. "The new generation is moving the needle far more quickly. But where's that talent going? It's going to the smaller players who have already invested in technology," Tainton said. Mid-tier and even larger carriers can't afford to sit back as the AI transformation happens. Building a comprehensive strategy around AI will require a multi-year roadmap. Many insurance carriers also embrace partnerships with insurtech companies to take their business to the next level. But Tainton stressed that carriers who don't take forward steps today will get left behind. "If you don't evolve with the technology, your market share will suffer," he said. "You can start small, but you've got to be agile and build rapidly. If you can show executives incremental wins and value, the change will come quickly."

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Which department will see the biggest impact of AI overall?

UNDERWRITING 52%

CLAIMS 53%

PRODUCT DEVELOPMENT 18%

FRAUD 37%

SALES 18%

customer services 36%

MARKETING 11%

There are four core elements in defining a successful AI strategy

Models and tools

Analytics Strategy

Data Capabilities

Organization and talent

Change management

Culture

The industry needs to be prepared for the avalanche of unstructured data from AI and machine-learning innovations, particularly sensory data, which Tainton called the industry's "biggest growth engine." Sensor technologies can gather and track data points, which AI can analyze and use for predictive insights. Tainton illustrated, "If I have a retail client with a processing plant, I can pick up on sensory data on the lifespan of a machine and what impact that could have on insurance. How will the machine breaking affect my client's bottom line? How do we get ahead and ensure we can service that machine before it breaks?" Embracing the future Insurance companies must position themselves for how AI and machine learning will reshape claims, distribution, underwriting, and pricing. Leaders must plan how they will build out their organization's culture, talent, and data capabilities for the long term to succeed in the future, AI-driven insurance industry. But they must also prepare for the short-term setbacks of emerging technologies. "Roughly 80% to 85% of all AI or machine-learning projects or initiatives fail. You're going in with an assumption you're going to answer a set of questions, and sometimes that doesn't always materialize in data sets," Tainton said.

clients faster," said Tainton. "Larger carriers initially struggled to make those investments in AI because of monolithic IT capabilities and arcane tech architecture. But now they're starting to make strategic investments as well. Mid-tier players are stuck because they can't make those investments or bolt AI into their existing infrastructure."