Insurance company executives are being pressured by board discussions, distribution channel partners and customer service requirements to more aggressively leverage the “shiny objects” that insurtech offers. Artificial intelligence (AI) is one of insurtech’s brightest contributions, and it seems natural for insurers to use advances in AI — including machine learning (ML), natural language processing (NLP) and robotic process automation (RPA) — to leapfrog competitors.
Unfortunately, not every insurer is ready for AI or able to take full advantage of the opportunities in this category of emerging technologies. There are, however, several ways insurers can prepare and evolve to a position of strength from which AI can make a strategic business impact.
1. Ditch Dirty Data
For a variety of reasons, insurers tend to have a good amount of “dirty” data, rife with inconsistent formats or standards, incomplete conversions resulting from merger and acquisition activity and data transfer from paper files. A proliferation of dirty data can put insurers in the untenable position of sacrificing whatever valuable intelligence may exist in historical files to a “Day Forward” strategy.
Insurers looking to prioritize AI projects must invest in cleansing bad data and improving data mastery. Those efforts will naturally include improving access to, and use of, both structured and unstructured data. The “magic” of AI gives the impression this technology is a silver bullet capable of maximizing the value of the unstructured data prevalent in handwritten forms, PDFs, images, email and text messages and social data, which increasingly inundate insurer workflows. However, the organization of clean and available data is a precursor to AI implementation.
See also: AI and Results-Driven Innovation
A recent report by Eric Weisberg and Mitch Wein of Novarica, “MDM in Insurance: Expansion and Key Issues,” details the need for insurers to invest more heavily in improving data mastery and hiring for positions such as chief data officer or data scientist, instead of purely tech talent. “Insurers are placing a priority on data initiatives to support their predictive modeling and AI programs,” Weisberg and Wein wrote. “High data quality is imperative for digitization where data is being exposed to outside parties. Existing and emerging data regulations are also driving a need for improved data governance. Chief data officers and multi-tiered data governance organizations are becoming more prevalent as data is increasingly being treated as an asset. Challenges exist with organization, resourcing, process and funding that can stymie the results of well-intentioned data programs.”
2. Cultivate the Right Culture
The fast-evolving nature of technology often means insurers are in a fluid state of decision-making about deployment. As innovation further penetrates traditional industry settings and transforms basic processes and products, insurers must decide if the organization’s culture and leadership are truly capable of committing to the journey of transformation, let alone arriving at the destination.
New tools, such as AI solutions, will demand new skills of managers who have built careers leading and inspiring people, and who understand the importance of change management to the organization. So, sorting out the boardroom and operational priorities of the CFO and CIO, or the VP of IT and COO, can help ensure solid business cases and implementation strategy for innovation — such as AI initiatives.
3. Prioritize the Policyholder
In addition to cleansing dirty data and strengthening internal change management, preparing to better leverage AI should include a re-prioritization around the policyholder and the customer experience. Insurers need more customer-centric processes from the ground up and a reinvention of existing products and processes that treat the policy as an attribute of the customer instead of the other way around. Customer acquisition is notoriously expensive, and insurers face the additional challenge of relating an age-old industry and product to a new generation of consumers. To be successful, the gap between old and new, and between company and customer, must be narrowed substantially.
AI can aid such efforts through innovations such as natural language processing (NLP), which recognize information included in voice conversations or recordings and then quickly and accurately deliver relevant policy files or information. Chatbots can also improve the speed of customer service interactions, and ultimately the speed at which policyholder concerns are resolved. Claims service is good example of a process in which insurers are already starting to see the benefits of incorporating AI solutions, and are using this technology to do everything from reporting first notice of loss (FNOL) to initiating claim processing, or even deploying an adjuster, if necessary.
See also: Future of Claims: Automation, Empathy
As insurers prioritize spending on AI initiatives and implementations, the danger is ignoring persistent shortfalls in important areas — such as data mastery, operations and even underwriting. And, it is important to recognize that innovation implemented in the form of an AI solution alone is not, and never will be, a viable strategy. AI can be an enabler of a strategy. But without clearly defined goals and a flexible operating model capable of supporting an evolving and demanding policyholder portfolio, even a successful AI implementation can end up as no more than a footnote.
Process automation, machine learning and other types of AI initiatives will continue to make for compelling business cases. To realize full potential and benefits, those tools should be focused on winning clients and implementing accessible, 24/7 customer service and operationally optimizating to support competitive differentiation. Cost savings from AI will typically flow as a by-product. But, without leadership, champions who embrace and drive change and organizational data mastery, the AI tools will be underused and unlikely to fulfill the promise of growth and service excellence.