AI-powered climate risk platform launched by Adaptive Insurance

Chatbots may be just the tip of the iceberg in technological advancement, but AMR’s report nonetheless noted its immense potential for market growth. “If you think about chatbots, it’s more about I ask the question and the chatbot will return an answer. A chatbot is a form of AI that uses natural language processing to analyze input such as questions and produce responses in a way that stimulates human conversation.

To mitigate these risks, insurers need to ensure full transparency and traceability in their pricing decisions and processes. The data lake used in Gradient AI’s AI-powered underwriting, called SAIL, allows insurers to gain predictive insights, enabling them to assess risks with speed and accuracy. The platform aims to improve the overall underwriting process, helping insurers capture more business and accelerate quote turnaround times. The insurance workforce is already accustomed to using low or no code apps, so it’s not a massive leap to see them using AI to augment tasks through AI colleagues and co-pilots.

The very promising opportunities AI opens to re/insurers rely on a harmonised interplay human expertise and intuition with creativity of generative AI. Akur8’s solution is explicitly developed for insurers, enhancing pricing processes across various lines of business, including workers’ compensation. Akur8 serves over 250 customers globally, including major industry ChatGPT App players like AXA and Generali, showcasing its significant impact and presence in the market. Chatbots have become even more popular in recent years thanks to programs such as ChatGPT, Gemini, Claude and countless others. Insurance companies use this technology in a wide variety of ways, including for customer service needs, to expedite claims processing and more.

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Our Insure AI solutions expand on this idea from reinsurance and transfer it to AI areas where new statistical models are used. This process fundamentally requires co-operation and transparency on the part of the customer. At a time when AI players are on different sides of the risk mitigation pendulum, Munich Re is trying to arm the industry with the good-old safety net of insurance.

From financial education to proactive communications, insurance agents can dismantle the seller stereotype. Samsung Galaxy mobile devices can help insurance adjusters increase efficiency and productivity at a lower cost of ownership. However, underwriting insurance chatbots are expected to see the most growth, reaching an estimated 30.6% CAGR as uptake increases. The National Institute of Standards and Technology (NIST) and the proposed Algorithmic Accountability Act in the US are developing frameworks to improve AI system management and governance, focusing on transparency and accuracy. This inclusive approach enhances the acceptance and adoption of AI technologies, promoting equitable outcomes. Involving diverse perspectives in AI decision-making ensures fairness, transparency, and effectiveness.

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Matthew Queen, attorney and owner of The Queen Firm, observes the evolution of AI in captive insurance with a more measured perspective. Queen remarked that AI is not yet capable of replacing the complex functions at the core of captive insurance—such as underwriting, claims management, and actuarial science—which he describes as the “bedrock” of the industry. He believes that while AI tools have certainly improved risk forecasting and research automation, they have not yet reached a level of sophistication that threatens to disrupt these crucial areas.

This shift allows workers to focus on complex, strategic tasks requiring critical thinking, creativity, and interpersonal skills. You can foun additiona information about ai customer service and artificial intelligence and NLP. Olivier Oullier, co-founder of Inclusive Brains & Chairman of the Institute for AI, Biotech Dental, explores how human-machine interfaces powered by generative AI are set to transform workplace inclusion and safety. Power outages are a significant problem for businesses in the US, affecting 15 million businesses each month and resulting in substantial financial losses.

Insurers could use AI to accelerate the claims process, simultaneously improving productivity, resolving a longstanding customer pain point and improving access to care. For example, healthcare providers and insurers could tap AI to handle unstructured data sets from multiple sources, significantly streamlining data entry. This step would increase accuracy, helping insurance claims adjusters make more accurate decisions and issue faster approvals.

• Text-based chatbots accounted for more than three-fourths of global market revenue in the insurance industry specifically.
• Scenarios are narratives about how the future might unfold, designed to raise awareness and stimulate discussion among stakeholders.
• Noting that these savings can be redirected towards business growth, employee support, and community engagement.

In an additive model, new weak learners (typically decision trees) are added sequentially, each one improving upon the performance of the previous models by correcting their mistakes (residuals). The company initially launched its Agentic AI platform within the pet insurance sector and is now looking to expand into new insurance lines. The platform utilises multimodal Large Language Model (LLM) capabilities to increase insurers’ output without additional labour, streamlining processes such as document review and compliance checks. AI is playing a pivotal role in enabling safer driving environments, which directly contributes to community wellbeing.

Luckily for us, this came relatively easy as the concept of embedding AI into our insurance process came from the company we use for site appraisals. Where our 80 controllers would take day trips to visit multiple sites, they can now conduct assessments from their desks. 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 model calibrates the best values and appraisals based on an expansive database of the values of equipment, machinery, buildings and other specialist assets. Algorithms assess our assets against [what’s] in the database to quantify our values as close to reality as possible.

With enough training data, algorithms can better analyze risk and predict outcomes, adding accuracy to risk models and pricing structures. Both traditional and Gen AI could empower organizations to enhance actuarial models, deliver personalized insurance cover, or even increase the pace of insurance claims. But the process of doing so appears to be slow, with testing and implementation processes often taking several months to complete.

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Additional risks, such as embedded bias and robustness of the results are either new or amplified by generative AI; so too are its capabilities to generate new content based on the training data. Our experience shows how important it is to have the necessary tech talent and expertise in-house to effectively develop, fine-tune and deploy such solutions at scale. As such, the rise of AI creates a huge demand for experts in the field in the race to harness the full potential of AI. The technology provider launched its London office in 2023, and now Gilthorpe said it has customers across thirty-five countries. His plan is not to expand (much) beyond these borders, but to instead deepen the customer base within them.

The company insured the first AI performance risk in 2018 and started to do so for large language models (LLMs) in 2019. It provides a comprehensive platform that supports insurers across different sectors, including property, casualty, life, accident, and health insurance. Accenture notes that insurers are also considering whether and how generative AI in insurance could address looming workforce gaps in claims and underwriting. But is it the next flavor of the month or a seismic shift in how we do business in the future?

According to KPMG’s 2023 CEO Outlook Survey, 57% of business leaders expressed concerns about the ethical challenges posed by AI implementation. While gen AI is poised to revolutionize a whole host of industries, it’s important that the technology is leveraged as a tool to bolster human know-how and expertise, and not the other way around. When it comes to trade credit insurance, gen AI will play a support role, saving time so that the experts can focus on customer relationships and skilled analysis. People will also continue to play a critical role supervising the entire process and making the decisions. The technology could supplement optical character recognition (OCR) to extract information from documents like invoices, credit notes and delivery notes to quickly verify that they match customer files.

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Among respondents using AI/ML models for severe convective storm risk, 81% felt they were ahead of the industry in adapting to climate-related challenges. This confidence level drops to 78% for those using stochastic models and 66% for those ChatGPT relying on traditional actuarial models. As natural catastrophes become more frequent and severe, a growing number of insurance companies are turning to artificial intelligence solutions for predicting and managing extreme weather  risks.

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• Artificial intelligence (AI) promises to supercharge productivity, improve customer experience and drive new business models, but the limitations and risks that come with technology have also come under the spotlight.
• Its evolving sophistication is reflected by the third of CEOs (32 percent) who are worried about increasing threats and the quarter (24 percent) who highlight vulnerable legacy systems.
• It can also free up much of the time spent on repetitive tasks, making use of people’s expertise more efficiently and further tailoring the support and guidance we provide to our customers.
• AI systems can quickly adapt to evolving regulations, ensuring ongoing compliance without extensive manual adjustments.

This traditional approach to scenario development is notably time-consuming and resource-intensive. While they agreed AI is a seismic shift, there were concerns about proving ROI and external use cases. According to Accenture, insurers are assessing AI from an ROI standpoint, particularly in the insurance claims process. As they do, they are confronting concerns about AI’s viability from a business and consumer point of view.

With this in mind, insurers must ensure the seamless integration of AI in claims management from the outset, or risk discouraging consumers from embracing automated tools. Machine learning algorithms can analyse claims data to identify anomalies and potential fraud, which even the most experienced handlers chatbot insurance might inadvertently miss. This ensures that genuine claims are processed swiftly, while fraudulent activities are flagged for further investigation, protecting us and our customers. In the last year we have identified over £2m of savings without compromising the speed to settle genuine claims.

This streamlining of operations enables agents to tackle complex issues, ensuring a seamless experience. Artificial Intelligence (AI) is revolutionising the insurance sector by enhancing underwriting, claims processing, customer service, and product development. The technology has already shown its value in automating manual tasks, improving risk assessments, detecting fraud, personalising customer interactions, and enabling predictive pricing. One key issue is the integration of AI into legacy systems, which are often outdated and difficult to modernise. There are also concerns about data security and privacy, as AI systems require vast amounts of sensitive information to function effectively. Moreover, the regulatory landscape around AI in insurance is still evolving, creating uncertainty about how the technology can be implemented within existing legal frameworks.

A multifaceted approach to mitigating these risks helps establish a balance between leveraging this powerful technology while driving the development of ethical AI that aligns with our values and needs. Therefore, the focus on responsible use of generative AI and the prevention of biased outcomes – and wrong but plausible-sounding answers – through regular and stringent validation of AI models is paramount. On the operational side, generative AI is set to introduce significant digital workplace enhancements. We are collaborating with leading tech partners to equip our employees with AI assistants by embedding LLM capabilities into the workplace.

Text-based rather than voice-based has also been the most popular type of chatbot so far, and AMR projects that will continue to be the case. Text-based chatbots accounted for more than three-fourths of global market revenue in the insurance industry specifically. AMR expects technological advancements and rising adoption of chatbots by insurance companies to “provide lucrative opportunities for market growth” in coming years. The global insurance chatbot market has experienced such enormous growth since the advent of the COVID-19 pandemic that it’s expected to hit nearly $4.5 billion by 2032, according to a report by Allied Market Research. Insurers should involve diverse stakeholders in AI development and testing to ensure fairness and transparency. Clear communication about AI decision-making processes is crucial to build trust and accountability.

This question was asked at a roundtable discussion with various insurance industry executives a few months back. Surprisingly, not a single executive thought it was a “flavor of the month.” Rather, they all saw the potential in this game-changing technology. Allied Market Research is the full-service market research and business consulting branch of Delaware-based Allied Analytics LLP, founded in 2013. As the global leader in TCI, Allianz Trade is investing in emerging technologies like gen AI to constantly improve our customers’ experience. Today and in the future, our promise to secure your trade ensures your business can grow in confidence.