This interview analysis is sponsored by Deloitte and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
With the fourth anniversary of COVID lockdowns across the world this month, business leaders are taking stock of how far many severely impacted industries have come in their respective recoveries, not the least of which includes insurance spaces.
In the initial impact of the pandemic, most of the reporting and insights for insurance leaders focused on the immediate fallout and ensuring work-from-home continuity of services and production – strategies primarily focused on mitigating damage rather than a long-term strategic vision for the future, given the circumstances. One would think the pandemic insurance market would have been a defensive one, trying to make the most of what would probably turn out to be a global recession.
Yet soon, enterprise leaders across the financial services sector began to realize a significant opportunity in a newfound embrace of AI from their management teams, and with it, the ability to drive digital transformations across their organizations. Fast forward to March 2024, and it’s clear that – even compared to most financial services spaces – the insurance industry was especially adept at turning their pandemic challenges into long-term digital wins.
Forecasting from the tail-end of the crisis in late 2021 from the SwissRe Institute, summarized by the chart below and cited in this Deloitte summary from that period, most effectively illustrates the momentum behind the industry’s comeback, mainly driven by AI-related technological innovations and agile adjustments to pandemic economic headwinds:
(Source: Deloitte.)
Deloitte senior insurance leaders Sandee Suhrada and Karl Hersch recently joined Emerj CEO and Head of Research Daniel Faggella on the ‘AI in Business’ podcast to further discuss the current trends, opportunities, and challenges of AI in insurance. Together, the pair advise insurance leaders on a broad range of fronts across the AI adoption process, from identifying ‘low-hanging fruit’ use cases to why insurance firms should prioritize customer service going into 2024.
This article examines three actionable takeaways derived from their conversation:
- Defining use case opportunities: An industry-specific criteria for identifying the ROI and data opportunity from emerging insurance use cases, likely across claims, underwriting, and fraud management workflows.
- Buy vs. Build: Important considerations for balancing in-house AI development with opportunities from partnering with outside consultants and AI vendors to help insurance leaders develop and enhance their burgeoning systems.
- Proactive AI- and generative AI-powered customer support: Leveraging new generative AI (GenAI) capabilities to expedite customer support workflows, personalized experiences, enhanced fraud detection to meet rising demand in customer expectations.
Listen to the full episode below:
Guest: Sandee Suhrada, Partner, Monitor Deloitte (UK)
Expertise: AI, machine learning, data analytics, strategic consulting, project management
Brief Recognition: A 20-year veteran of Deloitte, Sandee’s positions have consisted exclusively of strategic consulting responsibilities. In her current role, she delivers client business results via the practical application of AI and analytics. She holds an MS in Computer Science and Business Management from Penn State.
Guest: Karl Hersch, Principal, Vice-Chairman, and US Insurance Leader at Deloitte
Expertise: Insurance, financial services, strategy, digital transformation, strategic leadership
Brief recognition: Karl has spent his 30 years at Deloitte working within the financial services and insurance sector. In his current role as Vice-Chairman and US Insurance Leader, he heads the company’s entire insurance sector strategy. Karl manages Deloitte’s diverse financial and insurance operations to serve clients best. He holds an MBA in Finance from Columbia Business School.
Defining Use Case Opportunities
Sandee begins by stating how AI adoption is driving a drastic operational shift across the insurance sector by noting the opportunities available therein, noting that three opportunistic trends are particularly noteworthy:
- Expanding underwriting and claims assistance: Virtual assistants are helping with productivity gains and creating capacity for higher-value work, ultimately improving service experiences. These capabilities extend into fraud detection and management.
- Personalization of policies and services: AI can personalize sales interactions, tailor billing options, and provide customized operational support, improving the overall customer experience across insurance value chains.
- Transformation in how technology and operations function: Examples include analysis of existing code bases, understanding underlying logic, generating code scripts, and helping accelerate technical application development.
Sandee goes on to discuss the importance of organizations’ ability to identify the use cases that will help lead to successful digital transformations. She asserts to the Emerj executive podcast audience that, to make informed investments, it is critical that decision-makers and stakeholders:
- Define goals
- Analyze the impact on stakeholders
- Understand changes that AI systems bring to business operations
- Establish clear metrics for success
We will now examine Sandee’s specific advice for insurance use case assessment in two areas:
- Measuring ROI
- Defining the data opportunity
Measuring ROI from Insurance Use Cases
Since digital transformations have become more commonplace throughout financial services spaces, Sandee finds that insurance leaders, in particular, are quick to initiate the likely organizational identity crisis ahead of time: “The one question they keep asking themselves, which is a fundamental question, is: What do we want to be as we grow up and play in this space?”
Sandee refers to a bell curve framework she uses in the form of a horizontal scale chart to help potential clients structure their thoughts and answer these questions for themselves.
On the extreme left of the chart is what she refers to as “the active observer,” on the extreme right of the chart is “the emerging pioneer,” and in the middle is the “cautious adopter.” She tells Emerj CEO and Head of Research Daniel Faggella that 85 – 90% of insurance providers are in the middle or “moving slowly towards the right.”
She says this is because many of these organizations have metrics referred to as “known unknowns” – or variables that can’t be quantified or measured even if stakeholders are sure they’re having a potentially measurable effect on systems – around explainability, fairness, and transparency. Obviously, many of these firms are still in the exploration phase of adopting these technologies, and not quite ready for more significant investment.
If you’re unable to demonstrate the ROI of a solution, any efforts are ultimately unsustainable. Sandee and Karl both recommend that insurance leaders continuously ask their teams the following questions throughout the process:
- What are you trying to accomplish in the areas of business value, implications, and impact?
- Who will be affected, and how will operations change?
- How will performance and productivity metrics be measured and quantified to demonstrate critical ROI?
Defining the Data Opportunity
Making matters worse, many of the more frustrating “known unknowns” for financial services leaders driving successful AI initiatives include many principle indicators of long-term success with these technologies. Sandee explains at length how she advises business leaders to approach these metrics, keeping two critical factors in mind, the first being to “define the data opportunity.”
She emphasizes that it is an expensive proposition: Leaders must show the substantive ROI before they can sustain the transformation journey in perpetuity. Thus, defining the opportunity with data goals must be comprehensive, including detailed descriptions of what the organization wants to accomplish, the associated business value, the impact, and any more significant implications.
“You have to define the why, who will be impacted, and how business operations will change. Defining all these elements of trust actually helps you make the right decision in terms of where you want to be in your investment,” she tells the Emerj executive podcast audience.
Sandee then gives an example of helping a client build an underwriting system for specialty markets, hiring 30 new underwriters who would be augmented with AI-enhanced copilot systems to expedite their work. Leveraging such systems had immediately transparent business value, primarily where copilots can streamline workflows involving processing lengthy and complicated documents and sending quick confirmations to reduce onboarding times.
To judge the long-term benefits of such a system, Sandee asks business leaders to imagine what that underwriter’s job might look like when an AI-enhanced support system is automating the repetitive, manual tasks involved in their workflows as part of a larger product development cycle.
Once that larger vision is realized, Sandee emphasizes to business leaders that the next step is measuring success in terms of performance, productivity, and experience metrics: “The mantra everybody has to follow here is if you can’t measure it, you can claim it.”
Buying vs. Building
The second critical factor that Sandee and Karl advise leaders to keep in mind in approaching “known unknown” metrics of future AI success in financial services is whether enterprises should partner (or “buy”) with outside consultants and AI vendors to develop systems or try to “build” those capabilities in-house.
Among the many factors that separate GenAI from the capabilities that came before it is that – because GenAI has a user experience component, the impact of having more accessible access to customer feedback can help business leaders reimagine how they build and operate their enterprises. Sandee describes the value proposition therein as an “ecosystem play with external partners.”
Typically, building these systems requires partnerships with outside vendors. As these tools become more self-sufficient and more easily deployable straight “out of the box,” Sandee notes that not only will there be more open source models like AWS or Google Play, but it’ll also be more accessible than ever to build these capacities in-house.
While she concedes that most insurers are buying and not building these solutions, she encourages insurance providers to be holistic in their outlook. She notes three archetypal relationships with solutions providers that insurers can explore:
- Engaging with hyper scalers
In short, for many insurers, the easiest and most obvious option is to open an account with AWS or Google when they know their data needs are very typical compared to the competition. These insurers understand their data needs do not require specialization or would benefit from a direct vendor-enterprise relationship to ensure the agility of the system.
- Evaluating their whole existing enterprise solution vendor partnerships
Before reaching out to open source options from megatech firms, Sandee encourages financial services leaders to evaluate their existing enterprise solution vendor relationships. Given the nature of the AI vendor market, solutions with tremendous potential across the enterprise often work only in limited capacities based on their ability to market their solutions directly to industry-specific pain points. If business leaders can identify which vendors they’re already working with that can offer them more competitive services given the existing business relationship, Sandee emphasizes the benefits can far outstrip starting from scratch.
- Finding new partners
Sandee also advises financial services leaders that there are many benefits to keeping an eye out for new potential partners and data solutions, as the startup ecosystem is ever-evolving. For FIs with potentially enormous and unruly data stacks that typically see themselves as slow to enterprise adoption, the benefits of engaging with new and experimental partners could be well worth the risk.
Proactive AI- and GenAI-Powered Customer Support
Karl elaborates on the growing necessity for personalized and swift customer service and its impact on the deployment of AI solutions. He identifies GenAI as a pivotal ‘make-it-or-break-it’ factor in any significant digital transformation initiative.
He notes that pioneering companies such as Amazon are compelling industries with an online presence and onboarding processes to innovate, and highlights a growing dissatisfaction with traditional communication strategies and the perception of call centers as ineffective in addressing customer complaints. These elevating customer expectations are prompting leaders in the insurance sector to re-evaluate the effectiveness of their customer engagement strategies.
Karl also mentions that the surge in digital customer interactions, accelerated by the COVID-19 pandemic, has intensified these trends. He argues that insurance companies have an opportunity to focus on customer retention, growth, and profitability, suggesting that GenAI platforms can drive enhancements in these areas via:
- 24/7 Availability with chatbots and virtual assistants: AI-powered virtual assistants and other conversational AI tools can provide uninterrupted support for customer that can result in efficiencies throughout the entire organization if properly implemented.
- Hyper-personalization for tailored experiences: GenAI’s capabilities in analyzing customer data and interaction histories enable insurers to offer tailored and personalized support experiences, including real-time billing options and proactive notifications regarding potential issues (e.g., billing options, late payments).
- Fraud detection and enhanced security: Fraud remains a problematic and too-frequent occurrence in insurance. GenAI’s ability to analyze patterns and detect anomalies is much more sophisticated than that of traditional AI, significantly improving fraud detection and encouraging customer loyalty.
- Reduced wait times and expedited processes: By automating routine tasks and leveraging predictive analytics, AI can streamline processes such as claims assessments and approvals, translating to faster resolution times and improved customer satisfaction.
Insurers eager to take advantage of GenAI’s potential in this area should focus on three key things:
- Use case identification: Define specific areas where AI can add value – quick query resolution, personalized recommendations, and proactive fraud detection.
- Partnership strategy: Collaborate with specialized AI startups or work with established enterprise vendors to integrate AI capabilities effectively.
- Data-driven approach: Ensure you have clean, well-structured data to power AI algorithms for accurate insights and recommendations.
Throughout his podcast appearance, Karl underscores compliance challenges with GenAI and emphatically insists to insurance leaders that the technology represents a “don’t wait” situation, but one also calling for rigorous ‘test and learn’ processes.
Despite the need for caution, Karl emphasizes that insurance firms don’t need to give up on innovation. On the contrary, a focus on innovation throughout the AI adoption process can yield more unforeseen benefits – particularly given the clandestine nature of the insurance business:
“If you think about the sector we’re discussing right now, insurance, it’s a very misunderstood business. It often hides behind the veils of all the other businesses that we see and interact with on an everyday basis.
And it sometimes doesn’t get its due credit, and actually what it does to keep the world moving, keep society going because everything that happens happens because there’s an insurance policy sitting behind it in reality. So, there’s a real opportunity here to improve the attractiveness of the sector and to drive additional technology, talent, and business talent organizations as we’re changing the fundamentals of the business.”
– Karl Hersch, Principal, Vice-Chairman, and US Insurance Leader at Deloitte
