Large Language Models (LLMs) have demonstrated impressive capabilities, yet their deployment in high-stakes domains is hindered by inherent limitations in trustworthiness, including hallucinations, instability, and a lack of transparency. To address these challenges, we introduce a generic neuro-symbolic approach, which we call Autonomous Trustworthy Agents (ATA). The core of our approach lies in decoupling tasks into two distinct phases: Offline knowledge ingestion and online task processing. During knowledge ingestion, an LLM translates an informal problem specification into a formal, symbolic knowledge base. This formal representation is crucial as it can be verified and refined by human experts, ensuring its correctness and alignment with domain requirements. In the subsequent task processing phase, each incoming input is encoded into the same formal language. A symbolic decision engine then utilizes this encoded input in conjunction with the formal knowledge base to derive a reliable result. Through an extensive evaluation on a complex reasoning task, we demonstrate that a concrete implementation of ATA is competitive with state-of-the-art end-to-end reasoning models in a fully automated setup while maintaining trustworthiness. Crucially, with a human-verified and corrected knowledge base, our approach significantly outperforms even larger models, while exhibiting perfect determinism, enhanced stability against input perturbations, and inherent immunity to prompt injection attacks. By generating decisions grounded in symbolic reasoning, ATA offers a practical and controllable architecture for building the next generation of transparent, auditable, and reliable autonomous agents.

The emergence of Large Multimodal Models (LMMs) marks a significant milestone in the development of artificial intelligence. Insurance, as a vast and complex discipline, involves a wide variety of data forms in its operational processes, including text, images, and videos, thereby giving rise to diverse multimodal tasks. Despite this, there has been limited systematic exploration of multimodal tasks specific to insurance, nor a thorough investigation into how LMMs can address these challenges. In this paper, we explore GPT-4V's capabilities in the insurance domain. We categorize multimodal tasks by focusing primarily on visual aspects based on types of insurance (e.g., auto, household/commercial property, health, and agricultural insurance) and insurance stages (e.g., risk assessment, risk monitoring, and claims processing). Our experiment reveals that GPT-4V exhibits remarkable abilities in insurance-related tasks, demonstrating not only a robust understanding of multimodal content in the insurance domain but also a comprehensive knowledge of insurance scenarios. However, there are notable shortcomings: GPT-4V struggles with detailed risk rating and loss assessment, suffers from hallucination in image understanding, and shows variable support for different languages. Through this work, we aim to bridge the insurance domain with cutting-edge LMM technology, facilitate interdisciplinary exchange and development, and provide a foundation for the continued advancement and evolution of future research endeavors.

The insurance industry has undergone significant changes over the years. The integration of advanced technologies such as artificial intelligence (AI) has paved the way for further evolution, offering improved efficiency, reduced costs and enhanced customer experience. Various AI applications are currently in use in the insurance industry, ranging from underwriting to claims processing. AI can help insurers evaluate risk more accurately by analyzing large amounts of data such as historical claims data, credit scores and social media activity--thereby enabling insurers to offer personalized coverage to customers and price policies more accurately. It can also aid in detecting and preventing fraud by analyzing data patterns and identifying suspicious activity, which can help insurers save money by reducing the number of fraudulent claims they pay out.

As the Insurtech industry continues to evolve rapidly, it's essential to stay informed about the latest trends shaping the sector. To help you stay ahead of the curve, we've gathered insights from 14 industry experts on the most significant trends happening in Insurtech today. From personalized insurance offerings to the adoption of AI and blockchain technology, these trends are revolutionizing the way insurance is delivered and experienced by customers. A growing trend within Insurtech is the development of personalized insurance offerings. By utilizing AI and advanced analytics to analyze customer data, Insurtech companies can provide tailored insurance products based on individual needs and risk profiles.

One of the most significant changes in recent years in the insurance sector has been the incorporation of artificial intelligence (AI) into various phases of the insurance process. From underwriting to claims processing, artificial intelligence has the potential to transform the business by increasing efficiency, lowering costs, and improving customer experience. In this article, we will look at the function of artificial intelligence in insurance and its possible impact on the sector. Underwriting is an important part of the insurance process that involves assessing potential policyholders' risks and establishing the appropriate premium. This has traditionally been a time-consuming and labor-intensive procedure, but artificial intelligence has the potential to make it faster, more efficient, and more accurate.

As we welcome the new year, it's natural to reflect on the year that passed and look ahead to the challenges and opportunities that lie ahead, and more specifically how new technologies might impact the insurance industry. As always, we must separate the signal from the noise. For many, artificial intelligence is a perennial buzzword, but paradoxically, it appears the technology is largely still in its infancy in the insurance industry, and especially in the home insurance space. Regulators and insurers alike are understandably grappling with challenges created by the lack of model explainability, presenting challenges for the widespread use of AI to directly evaluate and price risk for homeowners insurance in the near future. Instead, major technological innovation in homeowners insurance in the coming year will likely come from solutions and tools designed to improve the ingestion and processing of data in ways that positively impact the consumer experience throughout their homeownership journey.

In 2020, the U.S. insurance industry was worth a whopping $1.28 trillion. The American insurance industry is one of the largest markets in the world. The massive amount of premiums means there is an astronomical amount of data involved. Without artificial intelligence technology like machine learning, insurance companies will have a near-impossible time processing all that data, which will create greater opportunities for insurance fraud to happen. Insurance data is vast and complex, composed of many individuals with many instances and many factors used in determining the claims.

The recent shake-out in NASDAQ-listed tech stocks spared few – not least insuretech disrupters such as Lemonade, Root and Hippo, who saw their market valuations slump an eye watering 85-90% from their peaks at one point. It wasn't difficult to see why, given aggressive and ongoing interest rate moves by the Fed and loss ratios (measuring claims incurred as a proportion of premiums sold) heading in the wrong direction. This in turn led to a substantial negative impact on earnings. Indeed, data from Capital IQ showed Root, Lemonade and Hippo collectively wracked up $1.1bn in net losses in 2021 vs. $474m two years earlier. Yet, if the travails of Lemonade, Root and Hippo offer a salutary lesson in frothy market valuations, they've also left the door open for traditional insurance providers to recapture (using third party software providers) lost market share.

Intelligent automation drives the best ROI for repetitive, standardized, and attention-demanding workflows. Claims management is a great example of such. Largely paper-based and rarely end-to-end digitized, the claims management process can eat up to 50%-80% of premiums' revenues. Being primarily manual, claims processing is also prone to errors and inefficiencies, which further drive up the insurers' operating costs. As McKinsey stated at the beginning of 2019, larger insurance carriers haven't quite addressed the costs of services delivery: In particular, the increase in connectivity--telematics and onboard computers in cars, smart home assistants, fitness trackers, healthcare wearables, and other types of IoT devices--now allows insurers to automatically collect more comprehensive data from customers.

In 2020, the U.S. insurance industry was worth a whopping $1.28 trillion. High premium volumes show no signs of slowing down and make the American insurance industry one of the largest markets in the world. The massive amount of premiums means there is an astronomical amount of data involved. Without artificial intelligence technology such as machine learning, insurance companies will find it nearly impossible to process all that data. This will create greater opportunities for insurance fraud to occur. Insurance data is vast and complex.