AI in insurance: Balancing innovation with due diligence Samsung Business Insights

Different types of policies that could provide coverage for claims arising out of the use of AI are outlined below. This creates mutual benefits for the partners and Majesco’s customers, enhancing operational intelligence across the insurance industry. Accenture notes that insurers are also considering whether and how generative AI in insurance could address looming workforce gaps in claims and underwriting. When it comes to implementing AI, it’s important for insurers to take a crawl, walk, run approach. Insurers should continue to explore low-risk, high-reward AI use cases to help claims adjusters do their jobs more efficiently.

The most important factor in selecting an AI-powered predictive risk model is widespread adoption within the insurance industry, with 45% ranking this as their number one or number two priority. When asked which model they consider most accurate for predicting risk, 27% favored traditional actuarial models, 26% preferred stochastic models, and only 20% saw AI and machine learning models as the most accurate. The launch of the Majesco Copilot AI ecosystem is part of Majesco’s larger mission to foster innovation in the insurance sector by providing their customers with access to best-in-class AI solutions.

The company integrates seamlessly with existing claims management systems, enhancing overall efficiency without disrupting operations. Rohan Malhotra is the CEO, founder and director of Roadzen, a global insurtech company advancing AI at the intersection of mobility and insurance. Roadzen has pioneered computer vision research, generative AI and telematics including tools and products for road safety, underwriting and claims. Companies like Axa, Allianz, Tata, and Audi use Roadzen to provide a better auto insurance experience to every driver on the road. Previously, Mr. Malhotra served as the Chief Executive Officer of Avacara, an enterprise software and data analytics company that provided product development services to Fortune 500 companies. Mr. Malhotra holds a bachelor’s degree in engineering from NSIT, Delhi University, India and a master’s degree in electrical and computer Engineering from Carnegie Mellon University where he studied AI and robotics.

AI Adoption Grows for Extreme Weather Risk Assessment

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 company aims to drive innovation across the broader insurance landscape by applying its solutions to more workflows. 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.

It is entirely plausible that within a few years, AI will not only generate natural catastrophe scenario narratives but also produce synthetic hazard data for these scenarios, such as hurricane wind fields. Eventually, we might even see AI-generated catastrophe models capable of simulating probabilistic losses. The potential applications are as vast as they are exciting, and our engagement with this technology can unlock the door to new capabilities in catastrophe risk assessment.

AI explained: AI and UK insurance

Since 2002, Cake & Arrow has partnered with leading insurance and finance companies, including MetLife, Aflac, Citigroup, Travelers, Chubb, Amwins, and The General. To maximize ROI for AI investments, insurance companies should also ensure claims adjusters receive proper training on using it. Likewise, if they do not yet possess sufficient in-house expertise in related fields like data science, insurers should consider partnering with technology providers that have deep experience in the field. Insurers who carefully integrate AI into their claims processes will find themselves ideally positioned to maximize the ROI they seek. For starters, a global Workday study found that only 41% of surveyed insurance executives believe their organization has the skills to keep pace with emerging finance technology.

As losses from extreme weather increase, insurers are rapidly adopting AI risk assessment models, with one in four now using AI for convective storms and 18% for wildfires, according to a recent survey by ZestyAI. These collaborations bring cutting-edge AI solutions to Majesco’s clients, elevating the capabilities of its platform. Majesco, a leading provider of cloud-based insurance software, has announced the launch of its new AI ecosystem designed to streamline insurance workflows.

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. Power outages are a significant problem for businesses in the US, affecting 15 million businesses each month and resulting in substantial financial losses. Generative AI (GenAI) has taken the business world by storm, promising to transform industries with its potential $4.4 trillion impact on the global economy, according to ChatGPT App McKinsey. Clients can also connect with me through an inquiry or guidance session to discuss topics related to the future of insurance. Contact your local member firm to talk through insights from this article, or to discuss your unique technology and AI requirements. 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.

More from Risk & Insurance

Tech-driven product innovation such as embedded insurance and usage-based insurance may yield faster results, but long-term AI gains remain on the horizon. It showcases how leveraging AI for data analytics can lead to improved operational efficiency and cost reduction, further provoking conversation on the future of AI in the insurance sector. Insurance industry and it will likely force innovation in many areas.” Yet, a reliance on legacy systems poses a challenge to innovation. While existing technologies provided the level of support previously required, and gave stability during the global pandemic to help insurers weather macroeconomic pressures, the same systems could now be holding them back. And with several tech giants intent upon disrupting the insurance market, it’s clear that traditional insurance products are struggling to keep pace with emerging customer lifestyles.

Join the webinar to learn how Design Thinking techniques can bring insurance concepts to life, allowing insurers to capture richer, more actionable insights into customer needs and create more intuitive human-centric solutions. The regulatory landscape surrounding AI is also evolving, and captive insurance firms will need to stay informed to ensure compliance. Queen noted that improvements in one area of insurance may not necessarily translate to others.

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. As the insurance industry grapples with evolving climate risks, transparency in risk assessment models has emerged as a critical concern.

Among the other areas in which AI can be transformative for the insurance sector are improving underwriting processes, claims management, customer service and future trends prediction. The insurance industry thrives on data—much of it unstructured, complex, and dispersed across various platforms. GenAI excels at processing this type of information, making it invaluable for enhancing operational efficiency and customer engagement. While some companies have begun deploying GenAI for tasks like claims processing and underwriting automation, they’re often missing the bigger picture. To truly harness the transformative power of AI, insurers need a comprehensive strategy, that goes beyond isolated applications. AI algorithms can assess various factors, such as driving behavior and accident history, to create personalized insurance policies that reflect the true risk of each driver.

“We believe that building and maintaining strong, long-lasting relationships with our customers is essential to navigating the inevitable fluctuations of the insurance market. Below are several qualities to look for in a partner that has the experience and insights to help mitigate and navigate their insureds’ unique exposures, giving leaders the space to focus on their core operations. This push for transparency insurance bots extends beyond internal operations, with 79% of executives advocating for regulatory mandates requiring model transparency. When it comes to the perceived accuracy of these models, there’s a notable lack of agreement on which type of model is most effective for predicting risk, the ZestyAI study found. From financial education to proactive communications, insurance agents can dismantle the seller stereotype.

This approach eliminates the traditional 24-hour waiting period before coverage takes effect, ensuring timely protection and minimising financial disruption, the firm explains. Their combined expertise in AI, machine learning, and treasury management is revolutionizing fintech, optimizing operations, and advancing financial strategies. 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.

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. This translates to faster payouts for customers and allows Prudential to manage a higher volume of claims, ChatGPT he added. Of the leaders surveyed who have already adopted AI risk models, 81% believe they are ahead of their competitors when adapting to the challenges of climate change. In addition, 73% of insurance leaders also believe AI models will help to manage climate-related losses.

Looking ahead, Prudential plans to expand the use of MedLM and other AI technologies to other areas of its health business. Beyond the technical challenges, firms must consider the ethical implications of AI adoption. 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. While some experts caution against the overhyping of AI’s capabilities, others are optimistic about its potential to revolutionise risk management, underwriting, and operational efficiencies.

Gradient boosting machines (GBMs) are a powerful ensemble learning technique that builds a model incrementally by combining weak models (typically decision trees) to form a strong predictive model. The main idea is to minimize the errors made by the previous model iteratively, thereby improving performance. KPMG professionals align to regulatory and voluntary standards, such as the EU AI Act and the ISO 42001.

AI-powered systems analyze accident data, assess damage through image recognition to automate the claims process, and assess driving behavior for personalized insurance premiums. From back office to front office, insurance functions can see potential benefits in automating claims handling, enhancing fraud detection, and optimizing agent and contact center operations. For now, these tend to be human-in-the-loop processes — with potential to fully automate. This expanded partnership will enable AXIS to streamline key processes, particularly in submission clearance, and improve customer service delivery across its markets. GlobalData’s 2024 Emerging Trends Insurance Consumer Survey found that 39.2% of consumers around the world would be comfortable or very comfortable with an AI tool to decide the outcome of an insurance claim they have made. Making a claim is often one of the most stressful points at which policyholders interact with insurers, if not the most stressful time.

As Risk and Insurance notes, data availability and ownership — already significant challenges in this sector — will become even more acute as insurers embrace AI. Furthermore, whilst using LLMs helps to avoid introducing human cognitive biases, scenarios produced by generative AI may inadvertently reflect biases present in their training data or model code. And while LLMs can produce scenario narratives, they cannot currently do the quantitative bits very well, such as estimating losses or evaluating business impacts.

The KPMG 2023 Insurance CEO Outlook also highlights a significant degree of trust in AI with 58 percent of CEOs in insurance feeling confident about achieving returns on investment within five years. Michael Jans, the “Godfather of Modern Insurance Marketing,” has distilled 27 years of experience into a simple, powerful guide that cuts through the clutter and delivers real results. AI use may result in significant additional regulatory burdens for insurance producers. Greg Cole, Head of Claims, AND-E UK explains some of the initiatives that are moving the dial on customer-centric service.

Insurers have also begun incorporating AI capabilities into other facets of the business, such as underwriting and the investigation of suspected fraud. As AI continues to impact how insurers are conducting business, various states are responding with regulatory frameworks to address purported risks. Accordingly, a patchwork of guidance has emerged, focused on governance, oversight, and disclosure regarding the use of consumer data and AI technology.

The company plans to use the newly raised funds to further develop its platform, allowing insurance agencies to improve their workflows, offer better customer experiences, and scale their businesses with increased efficiency. A significant proportion of global consumers would be happy for an artificial intelligence (AI) tool to determine the outcome of their claim, according to a GlobalData survey. As AI adoption continues to gain traction, insurers must make sure their solutions are able to deliver on the customer experience as tolerance for failure will be limited. It is important, however, to ensure transparency in the use of AI for decision-making processes. By clearly communicating how AI is used to make decisions, insurers can build trust and ensure customers understand how their information is handled and how decisions are made.

Shifting Covid Goalposts Sends Travel Insurers into Retreat

For more information, please see dacheng.com/legal-notices or dentons.com/legal-notices. The Earnix report highlights insurers’ struggle with legacy infrastructure that hinders collaboration and innovation, with 47% of executives citing siloed systems as a significant impediment. Almost half (49%) of insurers have incurred fines for compliance lapses, spurring renewed attention to regulatory tools and frameworks.

As the technology matures, the captive insurance industry stands to benefit from deeper insights and more sophisticated tools—ushering in a new era of innovation and efficiency. 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. “AI-driven models offer predictions that far surpass traditional underwriting methods,” Schmalbach noted, highlighting AI’s capability to process vast datasets and provide insights that are more accurate and timely than ever before. As AI becomes more integral to the way companies do business, policyholders need to determine whether their patchwork of policies protects them from their AI risks.

This is according to climate and property risk analytics firm ZestyAI which surveyed 200 insurance leaders on extreme weather, including storms, and AI. However, stochastic models remain the most popular approach for storms with 45% saying it is their go to tool and traditional actuary models based on historical data are favoured by 54% for wildfires. Alan said it has facilitated 900 conversations between its users and Mo over the past few weeks. But given that 680,000 people are currently covered by Alan’s health insurance products, Mo is quickly going to become a widely used healthcare-related AI chatbot. It will be interesting to see how people react to this new feature and how Alan tweaks the bot over time. Despite varying adoption rates, there’s a growing consensus on the benefits of AI in insurance, the survey shows.

As the corporate use of AI becomes more widespread, business leaders should be proactive in assessing their company’s AI exposure and the potential coverage issues under existing policies. First, you should identify every way in which your business relies on AI and all representations your company makes about its use of AI and AI capabilities. Second, you should analyze the potential types of claims that might arise from your specific uses of AI. Finally, you should work with your broker and/or attorneys to thoroughly review your insurance policies to minimize any potential coverage issues or gaps for AI-related liabilities.

How are insurers approaching AI?

“AI currently excels at automating repetitive tasks and assisting professionals in the captive insurance sector with routine activities. However, when it comes to more nuanced tasks such as deliberating what data to use for ratemaking, or issuing underwriting credits, AI remains largely supplementary, rather than a replacement for human expertise,” he said. In the past few years, artificial intelligence (AI) has made waves across various industries, offering new tools and capabilities that have transformed traditional practices.

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. Only 20% considered AI and machine learning models to be the most accurate, but 27% of respondents believed that a combination of different models offers the best risk prediction. Alan has long offered its customers a chat interface that lets them ask a question to a doctor and get an answer within 15 minutes or so. The next logical step these days would be to leverage artificial intelligence for medical conversations, so Alan is adding a virtual assistant called Mo to the chat feature. He is experienced in resolving a wide variety of commercial matters both at trial and on appeal. He also focuses a large share of his practice on insurance recovery litigation and on helping policyholders obtain the coverage and benefits provided in their insurance contracts.

• Customers are concerned about privacy, data security, potential scams, and inaccurate responses without sufficient oversight.
• Successful pilots can then be scaled up, ensuring that resources are allocated to projects with proven potential.
• Clear communication, a strong relationship and emphasis on sustainability are just the start.
• Investing time in prompt engineering – the practice of carefully crafting inputs to elicit the desired outputs from generative AI – is therefore vital.

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. An IBM study has found most insurance industry leaders believe generative AI is essential to keep pace with competitors. The insurance industry relies heavily on data to market, underwrite and administer insurance products. Machine learning algorithms can analyse claims data to identify anomalies and potential fraud, which even the most experienced handlers might inadvertently miss.

Insurers must ensure the seamless integration of AI in claims management from the outset, or risk discouraging consumers from embracing automated tools. Those using it significantly in customer-facing systems report a 14% higher retention rate and a 48% higher Net Promoter Score, the survey found. Insurers leveraging GenAI across direct, agent and bank assurance sales channels are seeing significant improvement in sales, customer experiences and customer acquisition costs, the survey found. 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.

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. Cake & Arrow is an experience design and product innovation company that works exclusively with the insurance and financial services industries. Our human-centered design approach helps carriers, distributors, and insurtechs create transformative digital experiences that drive results.

This process is repeated for several iterations, with each new model improving upon the last. 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. Investment rose from £2.1bn in 2018 to $8bn in 2019 and £8.8bn in 2020 before peaking at $16.5bn in 2021. Adaptive Insurance is one of the first companies to emerge from Montauk Climate, an incubator focused on climate technology.

In addition to the risk of error with AI, there are other risks that we consider insurable. In the case of generative AI, we are looking at the risk of copyright infringement and discrimination. You can foun additiona information about ai customer service and artificial intelligence and NLP. For both scenarios, we are currently cooperating with clients to structure specific insurance solutions. 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.

By combining deep industry and functional knowledge with the right technologies, KPMG firms can help you to unlock business value and harness the full power and potential of AI with speed, agility, and confidence. KPMG professionals are experienced in developing proof-of-concepts and scaling these into integrated digital solutions. And these processes have been used internally to review and enhance KPMG firms’ capabilities. 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.

In part 4 of our 2024 drug trends series, we’re evaluating workers’ comp high impact drug class patterns and strategies to address the challenges these cost drivers are creating. Download the report to equip yourself with the knowledge to thrive in this new era of insurance. “Data privacy is a significant concern,” Schmalbach acknowledged, and it will be vital for firms to implement stringent safeguards to mitigate this risk.

Semantic Feature Analysis SFA for Anomia in Aphasia: How-To Guide

For example, one gesture in a western country could mean something completely different in an eastern country or vice versa. Semantics also requires a knowledge of how meaning is built over time and words change while influencing one another. There are several different types of semantics that deal with everything from sign language to computer programming. In the compiler literature, much has been written about the order of attribute evaluation, and whether attributes bubble up the parse tree or can be passed down or sideways through the three. It’s all fascinating stuff, and worthwhile when using certain compiler generator tools. But you can always just use Ohm and enforce contextual rules with code.

• As mentioned earlier in this blog, any sentence or phrase is made up of different entities like names of people, places, companies, positions, etc.
• Moreover, analyzing customer reviews, feedback, or satisfaction surveys helps understand the overall customer experience by factoring in language tone, emotions, and even sentiments.
• Another approach is to just treat contextual rules as part of the semantics of a language, albeit not the same semantics that defines the runtime effects of a program.

Relationship extraction is a procedure used to determine the semantic relationship between words in a text. In semantic analysis, relationships include various entities, such as an individual’s name, place, company, designation, etc. Moreover, semantic categories such as, ‘is the chairman of,’ ‘main branch located a’’, ‘stays at,’ and others connect the above entities. If combined with machine learning, semantic analysis lets you dig deeper into your data by making it possible for machines to pull purpose from an unstructured text at scale and in real time.

Semantic Analysis Techniques

QuestionPro is survey software that lets users make, send out, and look at the results of surveys. Depending on how QuestionPro surveys are set up, the answers to those surveys could be used as input for an algorithm that can do semantic analysis. The very first reason is that with the help of meaning representation the linking of linguistic elements to the non-linguistic elements can be done. The main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related.

Semantic Analysis helps machines interpret the meaning of texts and extract useful information, thus providing invaluable data while reducing manual efforts. It is a crucial component of Natural Language Processing (NLP) and the inspiration for applications like chatbots, search engines, and text analysis using machine learning. Semantic analysis, expressed, is the process of extracting meaning from text. Grammatical analysis and the recognition of links between specific words in a given context enable computers to comprehend and interpret phrases, paragraphs, or even entire manuscripts.

Methodology

It’s static semantics, and you can use the techniques of denotational or operational semantics to enforce the contextual rules, too. Note that Ohm feels a lot like writing attribute grammars with semantic functions. However Ohm allows arbitrary JavaScript code in its semantic functions, which is more flexible than just slapping attributes on to parse tree nodes. Homonymy refers to two or more lexical terms with the same spellings but completely distinct in meaning under elements of semantic analysis. Relationship extraction is the task of detecting the semantic relationships present in a text.

The semantic feature analysis strategy uses a grid to help kids explore how sets of things are related to one another. By completing and analyzing the grid, students are able to see connections, make predictions, and master important concepts. In the above example integer 30 will be typecasted to float 30.0 before multiplication, by semantic analyzer. For example, imagine a man told a woman, “I care for you… a lot.” Wouldn’t that made the woman’s heart melt?

How To: Semantic Feature Analysis (SFA) for Anomia

Today, machine learning algorithms and NLP (natural language processing) technologies are the motors of semantic analysis tools. They allow computers to analyse, understand and treat different sentences. Today, semantic analysis methods are extensively used by language translators.

Semantic analysis systems are used by more than just B2B and B2C companies to improve the customer experience. Google made its semantic tool to help searchers understand things better. Semantic analysis creates a representation of the meaning of a sentence. But before getting into the concept and approaches related to meaning representation, we need to understand the building blocks of semantic system.

Humans interact with each other through speech and text, and this is called Natural language. Computers understand the natural language of humans through Natural Language Processing (NLP). Each attribute has well-defined domain of values, such as integer, float, character, string, and expressions.

To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes. It is usually applied to a set of texts, such as an interview or transcripts. The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly. The meaning representation can be used to reason for verifying what is correct in the world as well as to extract the knowledge with the help of semantic representation.

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