Bill Cecil’s Post

Times are changing rapidly, AL is increasing its importance in every sector, billions of rupees are being invested, the role of artificial intelligence is also increasing in the insurance industry.Artificial intelligence is likely to affect the entire landscape of insurance as we know it. Today, the insurance market is dominated by massive national brands and legacy product lines that haven’t substantially evolved in decades. This kind of stagnation has historically suggested that it is an industry ripe to be disrupted. $50 billion opportunity emerges for insurers worldwide from generative AI’s potential to boost revenues and take out costs AI technology offers insurance businesses large-scale financial potential from productivity gains, optimizing sales channels and digital advice, and delivering enhanced, personalized customer experience۔ Insurance businesses worldwide have a $50 billion dollar financial opportunity from generative AI to harness the technology in ways that could boost their revenues by as much as 20% and cut their costs by up to 15%, Generative AI will transform insurance distribution in four ways Early use of generative AI within insurance suggests the technology will transform distribution in four ways, including: ·       Agent productivity: The technology will help agents to navigate and produce content faster. It will reduce low-value interactions and provide coaching for more effective interactions with customers. ·       Customer self-service and sales support: An always-on virtual assistant will extend the availability of agents and help customers with product comparisons and digital purchases. ·       Hyper-personalization at scale: Tailored conversations, content, and offers will more readily respond to individual customer needs. ·       Business insights and decisions: Combining signals from unstructured data with structured data will yield new insights and aid in risk identification. Managing the risks Bain’s analysis also pinpoints key risk areas emerging from insurers’ developing use of generative AI including hallucination, data provenance, misinformation, toxicity, and intellectual property ownership. “As with any nascent technology, there will be risks,” said Sean O’Neill, leader of Bain’s global Insurance practice. “To manage risks, insurers should adopt a responsible AI strategy that includes short-term priorities, as well as a long-term vision enabling companies to build valuable AI capabilities to redefine their business operations.” Critical steps for insurance business leaders As generative AI continues to evolve, Bain urges insurance companies to take several critical steps to adapt to the fast-developing technology. These include aligning across business units on how AI can support business strategy, determining what to build internally and what to buy from vendors, ensuring that delivery teams are cross-functional, and designing an operating model that is adaptable.

💬 The world of insurance is no longer the same following the integration of AI in the insurance sphere. AI in particular group insurance has ushered a lot of advantages. As a tech leader and solution architect who is at the forefront of most of the digital transformations happening in insurance companies, AI got my attention because of its abilities to speed up, optimize, and automate a lot of processes that carriers devote resources for. "The Future of Insurance Underwriting by Deloitte" outlined that automation, alternative data, and AI are the key elements to be reconsidered in order the reposition underwriting for sustainable growth. AI is here to stay, though innovation seems inevitable in the group insurance sector but still will be impeded with some challenges in AI adaptation. Transparency concerns and regulatory problems potentially cause problems for AI systems. This necessitates transparency and fairness in underwriting decisions to build consumers trust and ensure that regulatory compliance. The next step toward the infrastructure capable of securing the citizens’ data safety is high-quality data accessibility. This issue relates to sensitive health information and data protection regulations (GDPR). Data privacy regulations are only one hindrance that insurers must overcome to move with AI in underwriting. While AI poses concerns of displacing human workers, it can be a key factor in upskilling and a platform for crafting new skill sets that allow for effective application of technological advances. Thereby, AI is a tool which together with the underwriters can avoid the repetitive routines and therefore themselves will focus on high quality activities, increase productivity and also customer satisfaction. When we look to the future, such successful implementation of AI in associations will demand advance strategic planning and alignment with company goals. People in the insurance industry are in a position to apply new data sources, such as synthetic data, to make their risk assessment more accurate and dynamic, which is why insurers can have a competitive edge in the market. As a tech star who aims to lead the way in innovation, I expect that AI will bring about the new future in group insurance too.

Last night I was on a panel about modernizing insurance at InsurTech New England. We took some really smart audience questions about AI that have me thinking. First rule of engagement: AI's not in charge, humans are. We can delegate responsibility to AI, but never accountability. What does that mean in an industry where what we are supposed to do is discriminate fairly? After all, insurance companies are really good at separating people and companies into risk pools based on characteristics that lead to loss (and hopefully only characteristics leading to loss). I've spent my career working on insurance products. We've always checked our input data for biases, and also our outputs for unexpected biases. We catch a lot before we launch. Regulators catch some too, and adjust the playing field accordingly. And other skews we unfortunately only see as we collect data from a newly launched product. The thing is, human biases are predictable. Human brains work how they work - we all live in one. We can scan for our known biases in how questions are asked and answered, and how data sources collect and report information. We can comb through outputs looking for unexpected results and correlations with demographics we didn't intend and that are often ghosts in the model from skews in the inputs. AI biases are not so predictable. They come in part from the data the model was trained on, which may not be our own data, and may not be assessable by outsiders. AI's biases also derive from innumerable statistical correlations that just aren't how a human brain calculates. That's why AI is so powerful, but also means the results that can't be intuited by us, with our messy wet brains. In this context, how do we check input and output for biases before we do harm to our customers (and our companies, and the insurance markets)? And how in the world does a regulator do this? We have to move forward with AI - not to be too over the top, but better insurance analytics saves lives and heartache and money. Always has, always will. And as a leader starting to deploy AI, I can't drop my accountability, ever. And that's a little terrifying in this moment, when we're starting to launch analytical power that we scarcely understand. Still, the humans are in charge. Or at least we think we are... With thanks to my fellow panelists and an engaged audience for a fun conversation: Meghan Titzer, CPCU, Rachel Switchenko, Jamie Luce, and the moderator with the mostest, Anna Kupik. #insurance #insurtech

Reimagining insurance with a comprehensive approach to gen AI August 22, 2024 | Podcast Insurance companies are at an inflection point with their generative AI use cases. Three McKinsey partners discuss the value of combining generative AI with other technologies. Despite forging ahead with generative AI (gen AI) use cases and capabilities, many insurance companies are finding themselves stuck in the pilot phase, unable to scale or extract value. Jörg Mußhoff sat down with Cameron Talischi and Khaled Rifai to discuss how organizations can escape “pilot purgatory” by leveraging traditional AI and robotic process automation in addition to gen AI; the importance of reimagining domains such as claims, underwriting, and distribution; and how to address data privacy and security concerns regarding intellectual property (IP) and other issues early on. This transcript has been edited for clarity. Jörg Mußhoff: To us, gen AI is not just hype. McKinsey has estimated that the total gen AI potential for the global economy is $4.4 trillion.1 Many insurance leaders are asking, “How do we get the benefits from first use cases, and how do we scale and make it real across geographies and business models?” Cam, could you start us off by telling us what you see in the overarching trends in gen AI and what applications and domains have the greatest potential impact for clients? Cameron Talischi: We’ve seen a lot of interest and activity in the insurance sector on this topic, which is not surprising given that the insurance industry is knowledge-based and involves processing unstructured types of data. That is precisely what gen AI models are very good for. In terms of promising applications and domains, three categories of use cases are gaining traction. First, and most common, is that carriers are exploring the use of gen AI models to extract insights and information from unstructured sources. In the context of claims, for example, this could be synthesizing medical records or pulling information from demand packages. In the context of underwriting for a commercial P&C [property and casualty insurance carrier], this could look like pulling information from submissions that come from brokers or allowing underwriters to more seamlessly search and query risk appetite and underwriting guidelines. The second category is the generation of content—namely, creative content. Think about it in the context of marketing or personalization. Again, in the context of claims, it’s communicating the status of a claim to a claimant by capturing some of the details and nuances specific to that claim or for supporting underwriters, and it’s communicating or negotiating with brokers. Use cases for coding and software development make up the last category. These are notable given the imperative for tech modernization and digitalization and that many insurance companies are still dealing with legacy systems.

Ready for takeoff: Generative AI in insurance Boards at every insurance company are talking about gen AI. But the discussion has changed from POCs to now rapidly executing ideas for responsible, secure, scalable, and commercially successful gen AI. The direction of travel !! Some insurers are already using gen AI in the back office for tasks like knowledge management. But since insurance is all about probability & statistics, we expect to see it soon across the entire enterprise. The next wave of deployment will include areas like risk scenario modelling & enhancing cognitive processes (alongside AI and RPA) where human intervention was previously necessary. Customer-facing uses are being created and we expect insurers to use gen AI to understand customer preferences and drive personalized products and services. First things first  For a successful gen AI-led transformation, insurers need a well-planned and well-communicated  change roadmap made by a cross-functional team, from an enterprise-wide point of view. At this stage, leaders would be well-advised to develop an ecosystem of partnerships to share gen AI expertise, since there is serious competition for capable talent. Tackling data demands  Data is the greatest challenge to getting gen AI right, since all generative large language models rely on high quality data and excellent prompt engineering for their success. Insurers will need to make sure that the way they train their gen AI models is transparent, fair, and accountable. This means knowing where their data comes from, where it’s housed, how secure it is, and whether their planned uses are ethical and responsible under todays’ data laws. To train gen AI models effectively, they will have to put old customer data into today’s context and use synthetic data to overcome gaps in their data that could lead to bias, as well as look for potential unfair correlations with external data sets that could deliver poor outcomes. Keeping compliant   The data challenge is where regulators are focusing their attention. Already there are laws in some US states (Colorado & California), and in Europe, that require insurers to, e.g., backtest some gen AI-delivered outcomes. And then there are industry agnostic laws governing gen AI, that capture insurers too, e.g. use of external consumer data. Expect regulation to get tighter and more specific. The regulation requirements need not be considered adversarial. Instead, they should be prepared to answer on data lineage, audibility, and governance structures.  As insurers begin to implement gen AI across their business, it is important to focus on fair & transparent outcomes, build a strong data foundation, and partner with expert vendors to help them achieve their goals.  ... But it isn’t all challenge and competition, insurers should feel positive that Gen AI can help them to better deliver for and delight their customers. Ben Podbielski Ramesh Sethi Maria Kokiasmenos Genpact

"Monetization in Insurance: Shaping the Future for the Insurance Industry" As technological advancements reshape our world at an unprecedented rate, the insurance industry stands at a critical crossroads. Modernization is no longer just a strategic option; it has become an imperative for insurance companies to stay relevant and competitive in today's rapidly evolving landscape. The driving forces behind the need for modernization in insurance are clear. Escalating customer expectations, dynamic market trends, and the need for integration and ecosystem connectivity are compelling insurers to transform their operations. Outdated legacy systems and redundant business procedures can no longer keep up with the demands of today's consumers, putting insurers at risk of falling behind more agile competitors. To navigate the path to modernization, insurers must strategize their renewal approach. Whether it's through greenfield technology, focusing on digital experiences, or progressive modernization, careful planning and execution are crucial. Additionally, fostering a modernization-friendly culture within the organization is as important as technological upgrades. Leadership must champion change, and employees must be willing to adapt. Cutting-edge insurance software solutions play a vital role in enabling modernization. These solutions streamline operations, enhance customer experiences, and integrate third-party services with unparalleled ease. However, insurers must also overcome challenges such as outdated systems and accumulated technical debt, the risk of replatforming, resource constraints, the relentless pace of innovation, and diversifying into new lines of business. To shape the future of the insurance industry, a strategic blueprint is essential. This blueprint ensures that modernization efforts are aligned with overall objectives and deliver maximum return on investment. The Vietnam Fintech Festival provides a platform for industry professionals to explore and discuss the monetization opportunities in insurance. By embracing modernization and leveraging the latest technological advancements, insurers can position themselves as agile players in the industry and meet the evolving needs of their customers.

I have no idea how insurance regulators are going to effectively regulate AI in insurance product building. I read the NAIC's model bulletin, "Use of Artificial Intelligence Systems by Insurers," adopted at their December meeting. It's actually quite good, focusing on governance, good data practices, and compliance with existing law for market conduct exams. But market conduct exams are every 3-5 years... Insurance product changes need to move a whole lot faster than that (yearly or twice a year per state). I'm afraid things will get really slow. I've worked directly with ~20 states on product filings. Most regulators: 🚸 truly want to protect customers 💸 have too few resources 💡 deal with increasingly complex products 🖐 are conservative with new product features (it feels worse to them to get it wrong than to slow down what comes to market) Your experience submitting a rate filing can be anything from: 🚀 wait 30 days, hear nothing, use the rates ⏱ 30-60 days of reasonable questions and/or small tweaks, get approval ⌛ wait almost 60 days, get a single easy question on day 59, restarting the 60-day wait. Repeat until you show up in person and talk out the issues. Produce more documentation and/or adjust your filing. Satisfy the regulator's concerns and/or perform for the (public, easily downloadable) record. Get approval. 😕 the last version, but you also have to drop in on the Commissioner first and tour the display of fire helmets from each town in the state (because the Commissioner is also the fire marshal). Watch while he tries on his favorite helmets and say nice things. Then go meet with the actuary, who then goes through your rating tables with you cell by cell with a highlighter marking specific cells they don't like "because it feels mean" (no actuarial principles involved). Then change your filing. Then get it approved. Then get subpoenaed to testify under oath in a rate hearing where the Commissioner is the judge, and the industry overall is wrong by design. (This only happened once, in one particularly wild state. It's also not an exaggeration.) Now add AI to the modeling. Model explainability, both in methodology and outputs, is going to be a challenge. We need to figure this out, both how to build products with AI that are interpretable and how they should be regulated, or we'll get AI everywhere on the fringes of insurance product building, but not where it can create better, fairer, safer products. #insuranceregulation #insurance #NAIC

This is an excellent explanation of typical carrier experiences at DOIs, illustrating the challenges of incorporating AI in insurance (a super important topic!). And these are the stories kosher for a Linkedin public post, heavily edited....

Governance and regulation are important elements in the insurance industry to enable our work with public consumers. How can we maintain pace as we push for innovation and advancements such as AI and ML? More work to be done.

Datos Insights Team Freestyling: AI in Insurance - Opportunities and Challenges   In our recent team discussion, we dove deep into the rapidly evolving world of AI in the insurance industry. Here are some key insights that emerged from our freestyling conversation:  1. The AI Funding Bonanza: We're seeing an interesting trend where major tech companies and global consulting firms are offering to fund or co-fund AI projects for insurance companies. This presents a golden opportunity, especially for mid-sized and smaller insurers, to dip their toes into AI waters with reduced financial risk. The catch? This "free money" might not be around for long, so it's time to act.  2. The Rise of the VP of AI and Chief AI Officer: As AI becomes increasingly central to insurance operations, we're witnessing the emergence of new C-suite roles like Chief AI Officer. What does this role entail? How is it shaping the future of insurance companies? These are questions we're eager to explore further.  3. Implementation Hurdles: Adopting AI isn't without its challenges. Our team highlighted the need for flexible, "plug-and-play" architectures that allow insurers to easily switch between AI models as technology evolves. We also discussed the unique challenges in testing AI solutions compared to traditional software. The age-old dilemma of being an early adopter versus waiting for technology to mature is particularly acute in the fast-moving AI space.  4. Vendor Lock-in Concerns: While integrated AI solutions from major cloud providers offer ease of use, they also raise concerns about over-reliance on a single vendor. How do we balance convenience with flexibility?  5. The Energy Elephant in the Room: As AI models grow larger and more complex, their energy consumption is becoming a significant concern. How does this align with the insurance industry's sustainability efforts?  6. Real-world Impact: We're already seeing tangible effects of AI implementation. For instance, one prominent Insurtech reported a significant headcount reduction due to AI-driven efficiencies. This raises important questions about the future of work in our industry.  As we navigate this AI revolution, it's clear that the opportunities are immense, but so are the challenges. At Datos Insights, we're committed to helping insurance leaders make informed decisions in this rapidly changing landscape.   What's your take on AI in insurance? Are you seeing similar trends? Let's continue this conversation in the comments!