Alan unveils AI health assistant for its 680K health insurance members

As a Data Analytics Lead in the insurance industry, he continues to pioneer new solutions that blend technical prowess with practical business impact. Beyond his work in insurance, Kanchetti is dedicated to mentoring the next generation of data professionals, sharing his knowledge and passion for making data-driven decisions that matter. AI-driven data analytics offers a groundbreaking solution to these long-standing problems. By integrating AI technologies such as machine learning (ML), natural language processing (NLP), and predictive analytics, insurers are now able to automate tasks that were once labor-intensive and repetitive. Similar to the ‘happy path’ concept, more routine claims can be partially or even fully automated. This frees handler resource to deal with more complex cases, improving operational efficiency, as well as  enhancing the customer experience by reducing waiting times and improving the accuracy of decisions.

The past year has brought key developments in the use of artificial intelligence in captive insurance. 27% of respondents believed traditional actuarial models to be the most accurate, while 26% favoured stochastic models. While Alan is better known as a health insurance ChatGPT App company, the French startup has always tried to offer more than insurance coverage. 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.

This gap underscores the slow progress in transitioning from traditional systems to advanced technology. Alan recently raised a $193 million funding round at an impressive $4.5 billion valuation. After France, Belgium, and Spain, the company last month announced plans to expand to Canada, where it will be the first new health insurance company in almost 70 years. But given that AI chatbots tend to hallucinate, healthcare professionals may not want to rely on inaccurate information or risk misdiagnosing a patient. This issue has come up in the news lately with AI-based medical transcriptions — eight out of ten audio transcriptions exhibited some level of hallucinated information, according to a study by a University of Michigan researcher.

Innovate or stagnate: Creating value from technology in asset management

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. 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 funding will fuel the company’s expansion into new insurance claims workflows, including Property & Casualty (P&C), Workers’ Compensation, and Travel claims, according to InsurTech Insights. The report points to the surge in interest in artificial intelligence (AI) as a likely driver of that excitement. GlobalData figures show that the AI market grew in value from $81bn in 2022 to $103bn in 2023 – a rise of over 27%, with a greater still compound annual rate of 39% forecast between 2023 and 2030.

Insurance Asia

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. For instance, AI-driven chatbots and virtual assistants are streamlining customer queries and claims processing, providing quick and CX-friendly responses 24/7. The insurance industry is poised to harness the latest technologies, including artificial intelligence (AI), to innovate and shape the future.

Dentons is a global legal practice providing client services worldwide through its member firms and affiliates. This website and its publications are not designed to provide legal or other advice and you should not take, or refrain from taking, action based on its content. 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.

Member firms of the KPMG network of independent 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. Financial services firms are performing better because of technology investments but now they need to fine-tune their digital transformation journeys. Market insights and forward-looking perspectives for financial services leaders and professionals.

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. These challenges contribute to doubts about whether AI will ever truly revolutionise the insurance industry in the way that many predict. The respondents who believe AI has already met expectations may represent those who have seen early successes in specific areas. For example, insurtech Lemonade has effectively used AI in customer service, using chatbots to handle routine inquiries and free up human agents for more complex tasks.

An overwhelming 90% of insurance executives agree that predictive risk models should be transparent. Adoption of these models varies depending on the specific peril being assessed, ZestyAI reported. For wildfire risk, traditional actuarial models remain the most common tool, used by 54% of insurers. Stochastic models follow at 30%, while AI and machine learning-based models are used by 18% of companies for wildfire risk assessment.

The ability of AI to process and analyze large datasets will enable insurers to better understand customer behavior, predict future trends, and offer more personalized services. Embracing ecosystems and platforms can help insurers adapt to market changes and even reduce the risk market disruption. 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. From a business perspective, there are promising use cases applying LLMs to efficiently analyse and process large documents and datasets powered by advanced natural language processing (NLP) applications. You can foun additiona information about ai customer service and artificial intelligence and NLP. Engineering high-quality data foundations is key to reaping the many future benefits LLMs may offer to drive efficiency across the insurance value chain.

This level of accuracy not only improves profitability for insurers but also makes premiums fairer for customers. Using the data, insurers can better assess risks and increase operational efficiencies. While the advantages of AI in claims settlement are undeniable, the paper also explores the ethical and regulatory considerations that insurers must address as they adopt these technologies. The use of AI raises important questions about data privacy, transparency, and fairness.

Since risk management is in the very DNA of the insurance business, it is perhaps not a surprise that many insurers feel due diligence will be necessary before embracing a transformative technology like generative AI in insurance. Given these caveats, many applications will necessitate an AI-assisted approach to scenario development. This process includes sense-checking and adjusting scenarios for specific business use cases, as well as translating narratives into measurable business impacts. LLMs should therefore be viewed as tools to assist with the heavy lifting of generating scenario narratives, rather than a turnkey solution.

For example, AI could help detect and prevent fraudulent claims or offer predictive insights. Seeking partnerships with AI solution providers that integrate with internal apps is a strong approach as well. AI is advancing quickly, with breakthroughs now spanning beyond language models to areas like weather forecasting, including hurricane landfall predictions[6].

Potential use cases for AI in insurance claims

Our solutions architects are ready to collaborate with you to address your biggest business challenges. Learn how insurance companies create a better employee experience by offering a flexible work environment. In addition, AI requires consistent access to a large volume of high-quality data to perform properly.

The rapid advancements in AI, notably generative AI, outpace existing legal structures, prompting a need for updated regulatory measures. Recent initiatives, such as the US President’s executive order, underscore the commitment to safe and secure AI deployment. This order, along with emerging global initiatives, aims to establish accountability and address the challenges posed by AI innovations in the insurance sector.

Advances in computer vision and telematics promise improvements in accident prevention and driving habits, resulting in fewer claims and reduced costs. These applications are making the mobility ecosystem smarter, faster, more transparent, and efficient by improving road safety. Through real-time alerts to prevent accidents, which lowers claim frequency and severity, both insurers, customers, and the larger society benefit. It could also mean making transparency the norm or simply asking people what they need and encouraging everyone to contribute ideas. At the very least, it’s investing in training and development that help employees understand how to apply these new technologies effectively to benefit both personal and organizational productivity. Insurance companies are already transforming their operations, exploring new technologies and in some cases leading the charge on AI.

Regulators are increasingly advocating for enhanced transparency and accuracy in how insurers assess risk,” the survey noted. Traditional actuarial models are considered most accurate by 27% of industry professionals, while 26% favor stochastic models. Their cloud-based software enables insurers to insurance bots 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. Today, we are exploring solutions to cover the various ways GenAI could potentially and randomly go wrong.

For example, advances in AI for catastrophic weather modelling may not have much bearing on general or professional liability insurance. As such, regulatory compliance must be tailored to the specific areas in which AI is applied. “Wielded by a qualified data engineer or data scientist, AI tools offer deeper insights into risk than ever before,” Queen explained. He emphasised that the use of AI in root cause analysis and risk forecasting opens the door to a “golden age” for captive insurance professionals, providing them with better tools to enhance decision-making. “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.

Insurers must implement robust governance frameworks and ensure transparent communication to reassure customers about the ethical use of their data. The research paper concludes by highlighting the need for insurers to stay ahead of the curve by embracing AI-driven innovations. Companies that are quick to adopt AI technologies will not only improve operational efficiency and reduce costs but will also gain a competitive edge in a rapidly evolving industry. They also know that innovation is a journey that requires ongoing effort, investment, and most importantly, a willingness to embrace change at all levels of the organization. While there are risks to every technology wave, the biggest risk could be missing the opportunity to shape what’s possible in insurance. While insurers and customers may not always see eye-to-eye on the priorities for generative AI, the technology presents significant opportunities for savvy insurers to jump ahead of competitors, IBM contends.

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. The swift development of AI has resulted in the increasing integration of AI technology in insurance claims management and insurance underwriting. In certain cases, AI has been used by insurers to streamline administrative work to improve efficiency, especially for day-to-day claims handling.

With this approach, Munich Re is able to determine the predictive robustness of the AI, quantifying, for example, the probability and severity of model underperformance. Herman Kahn, an American futurist, is often credited as one of the pioneers of modern scenario planning. During the 1950s and 1960s, Kahn used scenarios at RAND Corporation and the Hudson Institute to model post-World War II nuclear strategies. Additionally, industry standards from organisations like the National Association of Insurance Commissioners (NAIC) provide oversight and best practices for ethical AI use in insurance.

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 ChatGPT and improving the efficiency of scenario planning. The 1990s then brought the digital revolution and the birth of catastrophe models that enabled (re)insurers to simulate a large number of hypothetical natural disasters quickly and at scale.

Agentech, a leading AI-powered workforce solution provider for insurance claims, has successfully raised $3m in seed funding within 30 days. Sandeep Kumar is a technology leader in artificial intelligence for SAP enterprise solutions and analytics. GBM for insurance premium modeling can help the handling of complex model relationships with improved predictive power. The need to balance the model performance and follow the regulatory requirements is crucial, and it can be managed by using tools like SHAP to make it more transparent.

• 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.
• This expanded partnership will enable AXIS to streamline key processes, particularly in submission clearance, and improve customer service delivery across its markets.
• For now, these tend to be human-in-the-loop processes — with potential to fully automate.
• By focusing on ethics, compliance, and trust, the auto insurance sector is poised to tap into AI’s full capabilities while safeguarding the interests of its consumers.
• Claims processing is one of the areas in the insurance value chain ripe for automation, particularly concerning more straightforward claims.

Recent developments in artificial intelligence (AI), for instance, wouldn’t be possible without the data on which AI is based. With the new funding, Agentech intends to expand its AI-driven technology into adjacent claims processes like First Notice of Loss (FNOL), reserving, and file review. The $3m seed round highlights investor confidence in Agentech’s potential to disrupt the insurance industry. The company was co-founded by InsurTech veterans Robin Roberson and Alex Pezold, both of whom have a track record of successful tech exits worth a combined $182m. 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.