{"id":20115,"date":"2020-11-02T12:21:41","date_gmt":"2020-11-02T17:21:41","guid":{"rendered":"https:\/\/www.insurance-canada.ca\/?p=68670"},"modified":"2020-11-02T12:21:41","modified_gmt":"2020-11-02T17:21:41","slug":"ai-investment-in-commercial-lines-do-insurers-have-it-right","status":"publish","type":"post","link":"https:\/\/blog.lifeinsurance-orleans.ca\/index.php\/2020\/11\/02\/ai-investment-in-commercial-lines-do-insurers-have-it-right\/","title":{"rendered":"AI Investment in Commercial Lines \u2013 Do Insurers Have It Right?"},"content":{"rendered":"

New SMA Blog by Karen Pauli, Principal, Strategy Meets Action \u2014<\/span><\/h4>\n

Artificial Intelligence (AI) has been in almost every technology-based headline over the past 24 months. If an incumbent technology provider or a newly emerging InsurTech organization wants to grab attention \u2013 well, just insert AI in the first few lines of the description. Or, better yet, insert AI in the product or organization name. In fact, AI does hold exceptional business promise, and there are numerous proven use cases. But AI is a complicated topic.<\/p>\n

There are many sub-categories of AI, and one of the first steps in choosing the appropriate technology is to break down AI into consumable bites. SMA finds that there are six primary AI technologies in play within commercial lines organizations: machine learning (ML), computer vision, natural language processing (NLP), user interaction technology, voice technology, and robotic process automation (RPA). The big question is \u2013 which AI technologies drive the most value for commercial lines?<\/p>\n

Not surprisingly, there is a tug-of-war between AI for transformational purposes and AI for tactical purposes. According to commercial lines executives and managers, ML, RPA, computer vision, and NLP (in that order) will transform commercial lines the most. Given the general need for transformation across the insurance industry, one could conclude that the previously stated order of technologies would be where the industry is heading in terms of investment. But that is not the case.<\/p>\n

The actual investment order is new user interaction tech, machine learning, RPA, and NLP, with the remaining technologies following. Does this mean commercial lines insurers have gotten it wrong? The answer to that question is \u201cno\u201d with possible shadings of \u201ccould be.\u201d Much of the framing for this answer lies in the product mix.<\/p>\n

For the small business and workers\u2019 comp segments, new user interaction technologies such as chatbots and text messaging have been invaluable in contact centers. This impacts underwriting and claims by clearing tasks from work queues, thus freeing up technical expertise for more complex interactions. Billing benefits as well. Collaboration platforms and real-time videos proved highly valuable during the pandemic\u2019s height and continue to be highly worthwhile.<\/p>\n

Machine learning has universal value across product lines. Whether it be ML to improve straight-through processing for less complex lines, such as small business and workers\u2019 comp, or to provide decision support for complicated product lines, ML can contribute in all areas. The great thing about this is that investment in effectively adopting ML skills pays off across the enterprise.<\/p>\n

RPA is a technology that not only improves operational efficiency and expense management \u2013 important internal goals \u2013 but it also enhances customer and distributor satisfaction through rapid request fulfillment. Policy service, underwriting, and claims all gain value through RPA adoption. Because almost all commercial lines segments have repetitive processes, RPA skills are utilized universally.<\/p>\n

The \u201ccould be\u201d warning stated earlier comes in terms of computer vision and NLP. Both technologies have significant transformational value in commercial lines, ranging from turning aerial images into information to digitizing paper-based information sources. Prioritizing these technologies sooner rather than later is critical across all product segments.<\/p>\n

More than almost any other technology, AI technologies work best in combination. For example, NLP with RPA to increase process penetration. The industry is in early days when it comes to AI usage, and skill sets are still advancing. The \u201cgetting it right\u201d discussion is frequently dependent on product segments. But over time, value will be universal regardless of product complexity, albeit for different reasons. For additional information on all six AI technologies and survey results, see SMA\u2019s new research report, A.I. in P&C Commercial Lines: Insurer Progress, Plans, and Predictions<\/em><\/strong><\/a>.<\/p>\n

About The Author<\/strong><\/h4>\n

Karen Pauli, Principal at SMA, has comprehensive knowledge about how technology can drive improved results, innovation, and transformation within insurance operations. Karen has worked extensively with insurers and technology providers to reimagine processes and procedures to change business outcomes and support evolving business models. Her areas of focus include underwriting, claims, business intelligence and analytics, data sources, agency distribution, and customer management. Karen\u2019s real-world experience with digital transformation projects has given her unique insight into the changing customer and distributor experience in the digital age. In her more than 30 years at insurance companies, and in the technology research and advisory space, Karen has developed her talent for aligning business goals and perspectives with technology roadmaps to create competitive advantage.<\/p>\n

About SMA<\/strong><\/h4>\n

Exclusively serving the insurance industry, Strategy Meets Action (SMA) is an advisory services firm offering retainers, research, consulting, events, and innovation offerings to both insurance companies and solution providers. Learn more about SMA at www.strategymeetsaction.com<\/a>.<\/p>\n

SOURCE: Strategy Meets Action (SMA)<\/em><\/p>\n

Read the original article at Insurance-Canada.ca <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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