AI Adoption Grows for Extreme Weather Risk Assessment

Another study from Salesforce showed data and security worries were also holding back enterprises, with only 11 percent of surveyed CIOs saying the technology had been fully implemented. “Data privacy is a significant concern,” Schmalbach acknowledged, and it will be vital for firms to implement stringent safeguards to mitigate this risk. Insurers are increasingly aware of these challenges and see modern technology as a way to stay competitive. Mo is mostly a chatbot for now, but the company plans to give it the ability to remember more details and add personalization features to make it more proactive. Our vision is to make it a health companion that understands your context and your health history,” Lizée said.

• This is being applied to product design, tailoring insurance products and personalising recommendations to better meet the needs of our customers.
• Effective communication goes a long way in clearly understanding an insured’s business and future potential.
• Majesco, a leading provider of cloud-based insurance software, has announced the launch of its new AI ecosystem designed to streamline insurance workflows.
• While regulations continue to grow and develop regarding Al usage in insurance, there are several things we already know to be true.

Again, the KPMG global tech report reveals that better data management and integration have been the top benefits for 42 percent of respondents. They’re aware that data quality before cloud migration is key to effective AI applications, and that clean, well-organized data is essential for AI to ensure accurate, transparent and fair decision-making. This also links back to regulation as insurers with unstructured or fragmented data will face significant challenges in meeting new legislation and building trust in the market. KPMG in Israel assisted a large insurance company to develop a customer contact solution.

Insurers Rapidly Adopt Generative AI Despite Potential Risks

For insurance companies, transparent models enhance their ability to communicate effectively with policyholders about potential risk mitigation strategies. This open dialogue fosters trust and collaboration between insurers and their clients. Entering 2024, the opportunities and challenges of generative AI in insurance become more pronounced.

Some of the initial AI partners in the ecosystem include Charlee AI, CyberCube, Fenris, Gradient AI, and CoreLogic. Each partner provides unique AI-driven models, ranging from predictive claims analysis to cyber risk evaluation and property insurance tools. An example of failure of imagination was evident during Hurricane Katrina in 2005, when levees protecting the city failed, resulting in devastating flooding and nearly 2,000 fatalities. Despite the known risk of levee breaches in New Orleans prior to the event[3], such scenarios were not incorporated into catastrophe models used for risk management at the time. As a result, many (re)insurers unwittingly had large flood exposure concentrations in the city, which translated into substantial losses when the levees failed, resulting in the costliest insured loss on record at the time. Peter Schwartz, an early pioneer of scenario planning, likens the use of scenarios to “rehearsing the future”[1], where the objective is to run through (or practice) simulated events as if we are already living them.

Leveraging AI across the enterprise will be critical to improve both customer risk experiences and to implement the underlying IT tools that power those experiences,” he continued. After years of uncertainty, many insurers are ready to take the next steps to implement more effective strategies to grow their business and stay ahead of the competition. Our 2024 Industry Report surveys 431 global insurance executives on how they are responding to the critical developments that are shaping the future of insurance. An IBM study has found most insurance industry leaders believe generative AI is essential to keep pace with competitors. “While AI does automate certain tasks, it is more likely to augment human capabilities, allowing employees to focus on higher-value activities rather than replacing jobs entirely,” he said. The rise of AI-as-a-service platforms has made AI more accessible and affordable for firms which, Schmalbach argues, will help demystify the technology and dispel fears surrounding its adoption.

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The simple yet catchy term incorporates its “chat” capabilities with the fact that it involves a “robot” or bot. Additionally, industry standards from organisations like the National Association of Insurance Commissioners (NAIC) provide oversight and best practices for ethical AI use in insurance. One sector where gen AI has significant transformation potential is insurance, and specifically trade credit insurance (TCI). Let’s take a closer look at some of gen AI’s potential applications in TCI, as well as why human expertise remains critical when adopting emerging technologies. This approach eliminates the traditional 24-hour waiting period before coverage takes effect, ensuring timely protection and minimising financial disruption, the firm explains. By harnessing advanced AI and climate data, Adaptive Insurance offers businesses parametric coverage specifically designed for short-duration outages.

In terms of operational efficiency, AI can automate routine tasks such as data entry, claims processing, and reporting, leading to time and cost-savings. Schmalbach added that AI-driven analytics improve underwriting, ChatGPT pricing, and risk transfer processes. These improvements can lead to better financial outcomes for captive insurance firms, as they are able to make more informed decisions backed by data-driven insights.

By focusing on business outcomes, developing reusable technologies, and addressing ethical considerations, insurers can unlock the full potential of GenAI. Insurers need to strike a balance between exploiting existing assets and exploring new opportunities. GenAI offers avenues for both—enhancing current operations and opening doors to innovative business models. Member firms of the KPMG network of independent chatbot insurance firms are affiliated with KPMG International. No member firm has any authority to obligate or bind KPMG International or any other member firm vis-à-vis third parties, nor does KPMG International have any such authority to obligate or bind any member firm. KPMG combines our multi-disciplinary approach with deep, practical industry knowledge to help clients meet challenges and respond to opportunities.

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Fabien Vinas explains generative artificial intelligence, its opportunities and risks, and its use at Allianz Trade. Client zero The need for human thought and oversight, data analysis, critical thinking and decision-making is not disappearing. And so, while clients are looking for support, they’re also interested in the lessons learned along KPMG firms’ AI journey. The company also provides application programming interfaces for easier data integration, allowing organizations to combine their existing knowledge with the Gradient AI platform. The APIs can also be used to enable seamless integration of Gradient AI’s AI capabilities into existing products and workflows.

However, AMR again expects more uptake of voice-based insurance chatbots over the next decade, with a projected CAGR of 28.8%. That aligns with industry trends, which have seen well-known brands such as Allstate, IBM Watson and dozens of others launch insurance-specific AI chatbots over the past few years alone. Different stakeholders provide unique insights that can identify biases and mitigate unintended consequences.

She added that the company is dedicated to providing advisors with state-of-the-art digital tools, helping ensure Canadians receive the personalized insurance they require. BMO Insurance believes that this AI-powered assistant will enable advisors to focus more on their clients’ needs by simplifying the field underwriting process. This innovation, powered by Microsoft Azure OpenAI Service, ChatGPT App provides advisors with advanced digital support to help meet their clients’ needs. It forms part of BMO Insurance’s broader strategy to incorporate digital advancements that simplify client interactions. Earnix’s survey of 431 insurance executives shows 70% of insurers plan to deploy predictive AI models within two years, yet fewer than 30% have fully implemented AI today.

You can foun additiona information about ai customer service and artificial intelligence and NLP. From time to time, a new technology comes along with truly transformative potential, and generative artificial intelligence (gen AI) is no different. Gen AI is a type of artificial intelligence that can produce complex outputs such as text, voice, music, images or videos. KPMG professionals align to regulatory and voluntary standards, such as the EU AI Act and the ISO 42001.

• However, avoiding AI altogether may also expose insurers to the risk of missing out on potential opportunities and benefits, and losing competitive advantage.
• When it comes to implementing AI, it’s important for insurers to take a crawl, walk, run approach.
• While insurers and customers agree on the importance of using generative AI to deliver personalized pricing or promotions, many insurers haven’t yet translated that view into action.
• The integration of AI into captive insurance has already demonstrated several key advantages, particularly in risk management, operational efficiency, and customer satisfaction.
• It also means that we do not need to involve as many controllers because a lot of the process is automated – again reducing time and increasing efficiency.
• The process utilizes an initial model often with a constant prediction, such as the mean of the target variable for regression tasks like a decision tree with limited data depth.

The interplay between traditional insurers and InsurTech firms is vital for fostering sector-wide innovation and expanding coverage to underserved segments. Collaboration could also help steer insurance toward a more inclusive, customer-centric, data-driven and tech-enabled future. Consumer Duty presents an opportunity for insurers to refine their operations and improve customer outcomes. By leveraging AI, insurers can enhance their understanding of customer needs, streamline claims processing, detect fraud more effectively, and ensure compliance with new regulations. These advancements not only help meet the requirements of the Consumer Duty; they also position insurers as leaders in an increasingly competitive market.

According to AMR’s research, the global insurance chatbot market was valued at around $468 million as of 2023. It’s expected to achieve a compound annual growth rate of 25.6% over the next decade due to its rapid pace of growth. By adhering to these practices, insurers can foster trust, comply with regulations, and ensure the ethical and responsible use of AI technologies.

Adaptive Insurance is one of the first companies to emerge from Montauk Climate, an incubator focused on climate technology. Traditional insurance policies leave businesses exposed to significant losses due to coverage gaps that can lead to severe financial consequences. Noting that these savings can be redirected towards business growth, employee support, and community engagement. This process is repeated for several iterations, with each new model improving upon the last. It also notes that insurers “may have also been getting better at creating their own in-house data teams”, which could partly explain the drop-off. The levels of data analytics M&A investment within insurance in 2022 ($4.3bn) and 2023 ($1.8bn) were notable due to what had come before.

Health insurance companies or other intermediaries can deny requests for prescribed medications or refuse to pay for care after it’s provided. Park stressed the importance of prioritising relevant data and building the right platform to integrate internal and external data sources, ultimately delivering personalised services. She also detailed Prudential’s commitment to upskilling its workforce, starting with leadership and cascading down to other employees, ensuring everyone understands and can effectively utilise AI tools. Early tests have shown impressive results, doubling the automation rate of claim reviews and assessments with improved accuracy, according to Arjan Toor, CEO for health at Prudential.

Building The Future With AI At The Edge: Critical Architecture Decisions For Success

Traditional actuarial models are considered most accurate by 27% of industry professionals, while 26% favor stochastic models. Today, we are exploring solutions to cover the various ways GenAI could potentially and randomly go wrong. The significance of technology is para­mount – equally important is attracting top tech talents, which requires providing state-of-the-art tools and engaging them with challenging problems.

Interestingly, a significant portion of the industry – 27% – believes that a combination of different models offers the best risk prediction. This hybrid approach suggests a growing recognition that each model type has its strengths, and a multi-faceted approach may provide the most comprehensive risk assessment. Akur8, known for its next-generation insurance pricing solutions powered by transparent machine learning, has recently teamed up with the Louisiana Workers’ Compensation Corporation (LWCC). According to the company, the integration of AI models is simplified through Majesco’s GenAI-powered infrastructure, which enables AI partners to easily embed their solutions into the Majesco workflow. An earthquake in Silicon Valley damages the primary and backup cooling systems of several key data centers, leading to overheating and failure of critical servers and storage units.

Scenarios are narratives about how the future might unfold, designed to raise awareness and stimulate discussion among stakeholders. In the (re)insurance industry, scenario analysis is a cornerstone of risk management, crucial for understanding tail risks, identifying emerging risks, strategic planning, and managing risk aggregations. When it comes to implementing AI, it’s important for insurers to take a crawl, walk, run approach.

While regulations like the EU’s Artificial Intelligence Act are starting to address these concerns, insurers shouldn’t wait for legislation to dictate their actions. Implementing robust ethical frameworks and compliance protocols proactively can mitigate risks and build trust with customers and regulators alike. KPMG firms are excited about AI’s opportunities and equally committed to deploying the technology in a way that is responsible, trustworthy, safe, and free from bias. KPMG Trusted AI, is our strategic approach and framework to designing, building, deploying, and using AI solution in a responsible and ethical manner so we can accelerate value with confidence.

By utilizing a variety of AI techniques to reduce the number of calls from customers, the organization aims to improve customer satisfaction and increase the efficiency of agents. Using a using Natural Language Processing (NLP) and a classification algorithm, KPMG helped the client to analyze and then categorize calls to the support center. Overall, the analysis showed that many of the queries could be handled more effectively through a self-service solution.