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DCO Insurance Benchmarks the Industry’s Digital Transformation Progress

DCO Insurance Benchmarks the Industry’s Digital Transformation Progress

After a slow start, the insurance industry is embracing digital transformation – and this digital event highlighted how the sector’s data and analytics executives are leading the charge

Spurred on in part by InsureTechs intent on disrupting the industry, insurance companies have finally begun accelerating their digital transformations.

Research from Accenture shows that almost all insurance executives expect digital technologies to have transformed the sector by 2025. This month, Corinium’s Data Champions Online Insurance digital event shone a light on the trailblazing data and analytics executives who are leading the charge.

“[InsureTechs] are going to have a seat at the table and they are going to force everyone to innovate,” said Chris Monsour, Data Science Practice, Central Region Lead at AutoML platform DataRobot. “This isn’t a situation where being in an old, stodgy industry will protect you from innovation forever.”

This two-day gathering of top data and analytics leaders shone a light on how new technologies are helping insurers drive efficiencies, make better decisions and improve customer experiences. What’s more, it provided unique insights into what it takes to ensure these digital transformation projects deliver results.

Augmented Intelligence is Becoming More Widespread

Monsour used his session to highlight the many ways AI and augmented intelligence tools are transforming all aspects of the insurance industry.

InsureTechs such as Lemonade may have made waves when they introduced AI into the pricing and quoting process for consumer insurance. But today, commercial underwriters can also benefit from AI tools. Since it still takes time to accurately assess and price high-value commercial risks, insurers sometimes find they are receiving more submissions than they can process. As such, AI systems can help these underwriters prioritize their workloads.

“That’s something that’s tailormade for predictive models,” Monsour said. “By definition, this is something that a human will not have a lot of time to look at.”

At the same time, AI-powered claims handling dashboards are increasingly being deployed to provide agents with fast and reliable insights to make these vital insurance processes more efficient.

Couple these use cases with cross-industry applications for AI around marketing personalization and customer engagement and it’s easy to imagine how different the industry could look in five years’ time.

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How NLP Could Rehabilitate Chatbots

Chatbots have earned a reputation as a ‘cost cutting’ technology that can often have a negative impact on customer experiences. However, Prashant Natarajan, Director, Data Science and Analytics at Unum Insurance Group, argued that Natural Language Processing (NLP) can be used to create chatbots that deliver superior customer experiences and meet real customer needs.

“The reason customers use these kinds of channels is because they want to leverage the new channels that are available,” he said. “They want it to be smoother and they want a superior digital experience.”

Although 47% of Natarajan’s audience said they use chatbots to support live insurance agents, just 25% had built their bots using NLP. Natarajan was keen to point out that it can be counterproductive to deploy old fashioned bots that aren’t fit for purpose.

“If a customer has had a bad experience, they may come back one more time,” he suggested. “If they have a second bad experience, they’re going to call in. They’re not going to use a bot.”

He argued that NLP must play a key role in creating a ‘minimum viable product’ chatbot that can meet customer expectations.

NLP can be used to identify the topic a customer is contacting a brand about, what actions the customer wants performed and what product or service their request relates to. Crucially, NLP technology can also conduct sentiment analysis to judge the tone of customer requests and tailor its response, passing the request to a human agent where necessary.

“Starting small is perfectly fine,” he concluded. “Not starting with NLP is just going to create further challenges down the line.”

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Overcoming Key Barriers to Digital Transformation

Tom Fletcher, VP, Data Analytics at Partner Re, shared his experiences of how best to secure buy-in for data and analytics projects and get company stakeholders to see the value of data-driven technologies. He noted that a large portion of analytics projects are never put into production in insurance companies. Then, he proposed a framework to help analytics leaders ensure their work drives business transformations.

Data science is sometimes seen as a ‘throughput’ processes focused on building a solution to a particular problem. But Fletcher argued that analytics leaders must be equally focused on starting projects with the right ‘inputs’.

What are the inputs?” he asked. “Did you start with an adequate business problem that truly needed to be solved? Do you have the requisite data to solve that particular business problem?”

“Did you have [someone with] subject matter expertise to help when weird things came out of the data?” he continued. “Further, when [you] deliver something that might be useful, is the business engaged enough to say, ‘You’re on the right path’?”

Insurance companies are increasingly waking up to the necessity of digital transformation. But unless data and analytics leaders can answer ‘yes’ to all these questions before they begin an analytics project, they’ll still struggle to deliver the best results. As the industry transforms rapidly over the next five years, data-focused executives must work closely with their colleagues to bring their companies on this journey and align their strategies with pressing business needs.

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To view all the presentations from this year’s Data Champion’s Online, Insurance digital event and discover even more essential insights, click here now.