Quarter of insurers using AI for storm risk assessments

Moreover, the EC argues that if the proposal is maintained and an eventual review – five years after its transposition – favours mandatory insurance, contractual freedom should be maintained now and in the future.

The past year has brought key developments in the use of artificial intelligence in captive insurance. The AI solution is specifically designed for field underwriting and offers real-time support to advisors. It automates research by providing instant access to key information, significantly reducing the time spent sifting through various documents. Traditional actuarial models are close behind at 42%, while AI and machine learning-based models are used by 23% of companies for this peril. This means that organisations need to be able to rely on the output and accuracy of AI models.

Insurers also face lengthy implementation timelines, with 58% reporting over five months needed to make rule changes—a timeframe that puts them at a disadvantage in the face of market demands. Updating underwriting rules remains complex, with only 30% able to make changes within three to four months. While insurers recognise AI’s potential for real-time decision-making, integrating it remains a challenge as many firms cite legacy tech as a primary barrier to transformation. This is according to climate and property risk analytics firm ZestyAI which surveyed 200 insurance leaders on extreme weather, including storms, and AI. We are interested in the latest news, new products, partnerships and much more, so email us at; -edge.net.

Over the last year, AI technologies have made noticeable strides in the realm of captive insurance. According to Marcus Schmalbach, the chief executive of RYSKEX, one of the most significant advancements has been in enhanced risk modelling. AI algorithms, driven by machine learning, ChatGPT have become increasingly sophisticated, allowing for more precise risk assessments and predictions. In the past few years, artificial intelligence (AI) has made waves across various industries, offering new tools and capabilities that have transformed traditional practices.

Steps To Training A GBM Model1. Training A Decision Tree On The Data

For example, when it comes to our risk assessment and grading of companies, brokers and our customers sometimes request more information to better understand our decisions. In this scenario, gen AI could help by providing a more comprehensive explanation of risk assessments in just a few clicks, and enable teams to spend more of their time sharing detailed analysis for each customer or transaction. The auto insurance industry is experiencing a transformative shift driven by AI reshaping everything from claims processing to compliance. AI is not just an operational tool but a strategic differentiator in delivering customer value. In claims management, GenAI can swiftly and accurately analyse vast amounts of unstructured data like medical records and legal documents. This accelerates the process, reduces human error, and improves customer satisfaction.

For AI to be trusted and adopted by insurers, stakeholders must be able to interpret AI decision-making processes. Artificial intelligence is becoming a key priority as insurance organizations navigate complexity in a fast-paced world. The company aims to drive innovation across the broader insurance landscape by applying its solutions to more workflows.

Schmalbach stressed the importance of adhering to ethical standards when using AI, particularly in terms of transparency, accountability, and fairness. “AI systems can be made more equitable than human decision-making processes,” he argued, but this requires proper oversight and design. Firms must be vigilant about avoiding bias in their AI systems and ensure that AI-generated decisions are explainable and fair. Schmalbach noted that AI can tailor coverage to meet the unique needs of captives, which enhances customer satisfaction and leads to higher retention rates. AI’s ability to streamline operations, reduce costs, and provide more customised offerings can significantly improve the competitiveness of captive insurers in the marketplace.

GlobalData

Using personally identifiable information (PII) in AI processes poses risks such as data breaches and unauthorised access. Consider an AI-driven pricing model for auto insurance that uses diverse factors such as driving history, vehicle type, mileage, geographical location, and other demographic information. While race, gender, or income might not be direct variables, proxy factors highly correlated with these characteristics could lead to unfair pricing models.

The embrace of AI technology is far from uniform across the insurance landscape, according to ZestyAI. Reinsurers and insurtechs are leading the charge, with 100% of respondents from these type of companies in agreement on AI’s benefits in managing climate-related losses. In contrast, national and regional carriers, along with farm bureaus, are more hesitant. Only 75% of national and regional carriers and 67% of farm bureaus recognize AI’s potential in this area.

Others have leveraged AI for fraud detection, where machine learning algorithms can quickly identify unusual patterns that might indicate fraudulent claims. However, these isolated successes are not yet widespread enough to convince the majority of the industry, signalling that while AI’s potential is clear, its full impact has yet to be realised on a larger scale. Generative AI, particularly LLMs, presents a compelling solution to overcome the limitations of human imagination, while also speeding up the traditional, resource-heavy process of scenario development. LLMs are a type of artificial intelligence that processes and generates human-like text based on the patterns they have learned from a vast amount of textual data. This not only streamlines the scenario development process, but also introduces novel perspectives that might be missed by human analysts. Manual claims processes result in not just high rates of denial, but lengthy delays and errors, as well.

Their cloud-based software enables insurers to modernise their operations and deliver customer-centric experiences. The offering allows seamless integration of AI models from various industry partners directly into Majesco’s workflows. If this event were to happen tomorrow, in hindsight you may think that the risk was obvious, but how many (re)insurers are currently monitoring their exposures to this type of scenario? This highlights the value LLMs can add in broadening the scope and improving the efficiency of scenario planning. Calculating insured values is a specialist, complex and time-consuming task – particularly for an automotive supplier such as FORVIA Faurecia, which equips one in every two vehicles globally with its products on average. Learn how insurance companies create a better employee experience by offering a flexible work environment.

Addressing risks and strategic decision making

Insurers are also keen on AI’s potential to offer more customized policies by leveraging data analytics, which can help tailor coverage more precisely to individual customer needs. AI advancements are enhancing underwriting precision, streamlining claims management, simplifying distribution, while elevating customer service through personalized experiences. With 79% of consumers expressing trust in fully automated AI claims processes, insurers are tapping into AI’s potential to create tailored insurance products that meet individual needs. As AI tools analyze vast data sets, they not only expedite processes but also improve fraud detection and introduce efficiency and accuracy in auto insurance. The evolution of artificial intelligence (AI), including the new wave of generative AI (Gen AI), is transforming numerous industries.

27% of respondents believed traditional actuarial models to be the most accurate, while 26% favoured stochastic models. Despite varying adoption rates, there’s a growing consensus on the benefits of AI in insurance, the survey shows. A significant majority of insurance executives (80%) agree that AI and machine learning are opening new avenues for profitable growth. Moreover, 73% believe that AI models help better manage climate-related losses, and the same percentage agree that carriers adopting AI models will gain a competitive edge.

Insurance M&A investment in data analytics in the first nine months of 2024 was $5.7bn compared to $1.8bn for the whole of 2023. You can foun additiona information about ai customer service and artificial intelligence and NLP. By identifying common elements across different use cases, insurers can develop reusable components that expedite AI deployment in new areas. This strategy minimises the need to “reinvent the wheel” for each new application, saving time and resources.

“Expectations are incredibly high in today’s current climate,” said David Guild, head of financial lines, MSIG USA. “Companies and their leaders must be thoughtful and controlled in their communications, conveying both competence and a clear vision on an ever-evolving world stage. Interestingly, factors such as regulatory approval (31%), proven ROI (27%), and model transparency (20%) rank lower on the list of priorities. “I can’t say what specifically was said, but the upshot is that the regulators don’t want to be in the middle of every decision.

By adhering to ethical standards, insurers can maintain public trust, comply with regulations, and use AI responsibly. As AI continues to evolve, employees will have opportunities to reskill, upskill, and gain new competencies in areas like data analysis and AI management. This lack of transparency in AI algorithms could result in discriminatory outcomes due to biases in the training data. However, the rapid advancement and widespread adoption of AI in insurance also bring new concerns, particularly regarding potential biases and ethical implications. GlobalData’s poll run on Verdict Media sites in Q found that the majority of insurance insiders (60.2%) believe AI has not yet met expectations but think it will eventually. However, 29.6% remain sceptical, doubting that AI will ever live up to the hype, while only 10.2% feel AI has already met the industry’s expectations.

She highlighted Prudential’s newly established AI Lab, a collaborative initiative with Google Cloud that provides a platform for the company’s 15,000 employees to contribute ideas and experiment with AI applications. This helps to democratise access to AI and foster a culture of innovation within the organisation. We can also organize a real life or digital event for you and find thought leader speakers as well as industry leaders, who could be your potential partners, to join the event. We also run some awards programmes which give you an opportunity to be recognized for your achievements during the year and you can join this as a participant or a sponsor.

AI also significantly improves our understanding of customer needs through advanced data analytics, enabling a more personalised approach. This is being applied to product design, tailoring insurance products and personalising recommendations to better meet the needs of our customers. However, chatbot insurance the IBM survey also revealed significant disconnects between insurers and customers regarding GenAI expectations and concerns. For example, insurers are focused on using generative AI to improve customer service, but customers prioritize getting the right personalized products.

Claims processing is one of the areas in the insurance value chain ripe for automation, particularly concerning more straightforward claims. While most insurers have started taking steps to integrate AI solutions in the value chain, insurtech DGTAL has gone a step further, developing completely autonomous AI agents. Reportedly, it is the first insurance-focused AI company to use AI agents as a core element of its claims platform DRILLER. Traditional AI solutions are programmed to provide a single response to a prompt, while according to DGTAL, its AI agents can operate real workflows and work together with other AI agents or human experts. Insurers can accelerate claims processing with the use of AI solutions, as these can scan vast amounts of data faster and increase accuracy. An increase in the speed of claims processing, as well as the ability to liaise with an agent 24/7, will naturally be beneficial for customers.

IBM: Insurance industry bosses keen on AI. Customers, not so much

For instance, AI systems equipped with telematics can provide drivers with detailed feedback on their driving habits, encouraging safer behavior on the road and potentially reducing accident rates. Ilanit Adesman-Navon, Head of Insurance and Fintech at KPMG in Israel, highlights how AI can be used to guide ‘next best offer’ in more sophisticated ways. AI can be trained to understand sentiment, empathize with the customer situation, then guide agents to the most relevant, personalized offers — all of which could be done in real time”. COVU, a company specialising in AI-native services for insurance agencies, has successfully raised $12.5m in equity and debt financing as part of its Series A funding round.

Engineering high-quality data foundations is key to reaping the many future benefits LLMs may offer to drive efficiency across the insurance value chain. Also, it is paramount to ensure the proper guardrails are in place before releasing new AI-powered solutions, also to gain the trust of our clients and make them part of this journey. Founded in 2012, the company specializes in providing AI solutions for the insurance industry, particularly focusing on automating underwriting processes and improving operation efficiencies.

“Quarterly and annual earnings calls provide a platform to discuss financial results and respond to investor questions. Investor presentations offer a more comprehensive overview of the company’s strategy, performance and outlook,” Guild explained. Effective communication goes a long way in clearly understanding an insured’s business and future potential. This allows for sustainable partners to develop coverage that fits and to work closely with their Claims team to understand the partnership in context. For financial companies and commercial businesses looking to keep pace with today’s risks and better understand their own exposures, finding the right insurer need not feel like an added weight. On the policyholder side, transparency empowers individuals to take proactive steps in managing their property risks.

It now wants to build a super app for all things related to healthcare and announced three new product updates on Tuesday morning, including an AI chatbot that’s vetted by doctors. “AI has an incredible capacity to transform the insurance industry by enhancing the capability of carriers to protect the assets and wellbeing of policyholders in an increasingly complex world. This enthusiasm is reflected in our research — the consensus among insurance leaders is that AI will be a crucial enabler for realizing profitable growth going forward,” stated Attila Toth, founder and CEO of ZestyAI. We also publish Artemis.bm, the leading publisher of news, data and insight for the catastrophe bond, insurance-linked securities, reinsurance convergence, longevity risk transfer and weather risk management sectors.. We’ve published and operated Artemis since its launch 20 years ago and have a readership of around 60,000 every month.

This aligns with the Consumer Duty principle of ensuring that customer outcomes are at the forefront of all business activities. However, in pursuing these AI-driven innovations, insurers cannot lose sight of the importance of building and maintaining customer trust. In fact, 77% of insurance CEOs said establishing customer trust will have a greater impact on their organization’s success than any specific product or service. This is especially critical given that consumer trust in the insurance industry is already shaky, with trust scores declining 25% since pre-COVID-19.

• Agentech, a leading AI-powered workforce solution provider for insurance claims, has successfully raised $3m in seed funding within 30 days.
• The company integrates seamlessly with existing claims management systems, enhancing overall efficiency without disrupting operations.
• This means that they can hallucinate, creating implausible scenarios that are not relevant to the world we live in.
• Cake & Arrow is an experience design and product innovation company that works exclusively with the insurance and financial services industries.
• AMR expects technological advancements and rising adoption of chatbots by insurance companies to “provide lucrative opportunities for market growth” in coming years.

Leadership teams acknowledge that AI could completely transform their operating models and ultimately, the customer experience. However, insurance organizations appear to be approaching the technology strategically and with cautious optimism. A new parametric insurance ChatGPT App platform, Adaptive Insurance, powered by artificial intelligence (AI) has launched with a mission to change how businesses safeguard against climate risks. Furthermore, the precision and reliability of AI operations depend heavily on the integrity of data.

Even when implemented, the pay-off from AI projects can be far less than hoped for by overexcited executives. Or perhaps Big Blue could simply listen to customers, only 29 percent of whom are comfortable with generative AI agents providing service, according to IBM’s figures. The study is based on a survey of 1,000 insurance c-suite executives and 4,700 insurance customers. CEOs in the survey were evenly decided on whether generative AI was a risk versus an opportunity although 77 percent who responded said generative AI was necessary to compete.