Generative AI is supercharging insurance fraud by making it easier to falsify accident evidence at scale and in rapid time. Insurance fraud is a pervasive and costly problem, amounting to tens of billions of dollars in losses each year. In the vehicle insurance sector, fraud schemes have traditionally involved staged accidents, exaggerated damage, or forged documents. The rise of generative AI, including deepfake image and video generation, has introduced new methods for committing fraud at scale. Fraudsters can now fabricate highly realistic crash photos, damage evidence, and even fake identities or documents with minimal effort, exploiting AI tools to bolster false insurance claims. Insurers have begun deploying countermeasures such as AI-based deepfake detection software and enhanced verification processes to detect and mitigate these AI-driven scams. However, current mitigation strategies face significant limitations. Detection tools can suffer from false positives and negatives, and sophisticated fraudsters continuously adapt their tactics to evade automated checks. This cat-and-mouse arms race between generative AI and detection technology, combined with resource and cost barriers for insurers, means that combating AI-enabled insurance fraud remains an ongoing challenge. In this white paper, we present UVeye layered solution for vehicle fraud, representing a major leap forward in the ability to detect, mitigate and deter this new wave of fraud.

Prediction uncertainty quantification is a key research topic in recent years scientific and business problems. In insurance industries (\cite{parodi2023pricing}), assessing the range of possible claim costs for individual drivers improves premium pricing accuracy. It also enables insurers to manage risk more effectively by accounting for uncertainty in accident likelihood and severity. In the presence of covariates, a variety of regression-type models are often used for modeling insurance claims, ranging from relatively simple generalized linear models (GLMs) to regularized GLMs to gradient boosting models (GBMs). Conformal predictive inference has arisen as a popular distribution-free approach for quantifying predictive uncertainty under relatively weak assumptions of exchangeability, and has been well studied under the classic linear regression setting. In this work, we propose new non-conformity measures for GLMs and GBMs with GLM-type loss. Using regularized Tweedie GLM regression and LightGBM with Tweedie loss, we demonstrate conformal prediction performance with these non-conformity measures in insurance claims data. Our simulation results favor the use of locally weighted Pearson residuals for LightGBM over other methods considered, as the resulting intervals maintained the nominal coverage with the smallest average width.

Prediction is one of the most important inferential tasks for actuaries since it forms the basis for many key aspects of an insurer's business operations, such as premium calculation and reserves estimation. According to Shmueli (2010), there are two key goals in data science and statistics: to explain and to predict. However, these two goals often warrant different approaches. For example, as demonstrated in Shmueli (2010), a wrong model, under some conditions, can even beat the oracle model in prediction, but the same cannot be said for explanation. This paper only concerns prediction. In the existing insurance literature, prediction is often performed using either a parametric approach or a non-parametric approach (e.g., Frees et al. 2014). In the parametric approach, the actuary posits a model, applies model selection tools to choose the "best" model, trains the chosen model, and finally makes predictions; see, for example, Claeskens and Hjort (2008) and Part I of Frees (2010). While this parametric approach has been widely applied in insurance, it has several drawbacks. First, the posited model may be misspecified, leading to grossly misleading predictions (Hong and Martin 2020).

Healthcare data is a valuable resource for research, analysis, and decision-making in the medical field. However, healthcare data is often fragmented and distributed across various sources, making it challenging to combine and analyze effectively. Record linkage, also known as data matching, is a crucial step in integrating and cleaning healthcare data to ensure data quality and accuracy. Apache Spark, a powerful open-source distributed big data processing framework, provides a robust platform for performing record linkage tasks with the aid of its machine learning library. In this study, we developed a new distributed data-matching model based on the Apache Spark Machine Learning library. To ensure the correct functioning of our model, the validation phase has been performed on the training data. The main challenge is data imbalance because a large amount of data is labeled false, and a small number of records are labeled true. By utilizing SVM and Regression algorithms, our results demonstrate that research data was neither over-fitted nor under-fitted, and this shows that our distributed model works well on the data.

Nobody goes to the circus to see the net. But when the high-wire gymnast slips, the net is suddenly the star of the show. So, don't think of me as being in the health insurance industry, the safety net of your life. I don't want you to stop reading. I'll tell you I'm an expert in customer service and have been persistently challenged by innovators in the retail and tech space who have conditioned the consumer to expect instant responses, instant results and instant products.

ABSTRACT As governments race to implement new climate adaptation policies that prepare for more frequent flooding, they must seek policies that are effective for all communities and uphold climate justice. This requires evaluating policies not only on their overall effectiveness but also on whether their benefits are felt across all communities. We illustrate the importance of considering such disparities for flood adaptation using the FEMA National Flood Insurance Program Community Rating System and its dataset of 2.5 million flood insurance claims. We use CausalFlow, a causal inference method based on deep generative models, to estimate the treatment effect of flood adaptation interventions based on a community's income, diversity, population, flood risk, educational attainment, and precipitation. We find that the program saves communities $5,000-15,000 per household. However, these savings are not evenly spread across communities. For example, for low-income communities savings sharply decline as flood-risk increases in contrast to their high-income counterparts with all else equal. Even among low-income communities, there is a gap in savings between predominantly white and non-white communities: savings of predominantly white communities can be higher by more than $6000 per household. As communities worldwide ramp up efforts to reduce losses inflicted by floods, simply prescribing a series flood adaptation measures is not enough. Programs must provide communities with the necessary technical and economic support to compensate for historical patterns of disenfranchisement, racism, and inequality. Future flood adaptation efforts should go beyond reducing losses overall and aim to close existing gaps to equitably support communities in the race for climate adaptation. INTRODUCTION Flooding constitutes nearly a third of all losses from natural disasters worldwide (Reuters 2022). By the end of the century, rising sea levels and coastal flooding are estimated to cost the global economy $14.2 trillion (a fifth of the global GDP) in damaged assets (Kirezci et al. 2020).

Artificial Intelligence has been making headlines lately as new tools that allow you to create art or content from text prompts are released to the public. The pace of AI-related innovations appears to be increasing as new tools are coming out faster than ever. The entire tech industry is paying close attention as every innovation can become a new disruption to the current way of things. We're going to look at how artificial intelligence works and how it's altering the tech industry in a major way--plus, how to get started investing with AI. At the most superficial level, AI is about using computer systems to handle tasks that humans have normally performed throughout history.

In order to gain a better understanding of the background to this problem, let us first introduce some fundamental knowledge about machine learning. Compared with traditional programming, one major difference is that the reasoning behind the algorithm's decision-making is not defined by hard-coded rules which were explicitly programmed by a human, but it is rather learned by example data: thousands, sometimes millions of parameters get optimised without human intervention to finally capture a generalised pattern of the data. The resulting model allows to make predictions on new, unseen data with high accuracy. To illustrate the concept, let's consider a sample scenario about fraud detection in insurance claims. Verifying the legitimacy of an insurance claim is essential to prevent abuse.

The media makes it sound like magic. Reading this article will change that. It will give you an overview of the most common types of problems machine learning can be used for. And at the same time give you a framework to approach your future machine learning proof of concept projects. How is machine learning, artificial intelligence and data science different? These three topics can be hard to understand because there are no formal definitions. Even after being a machine learning engineer for over a year, I don't have a good answer to this question. I'd be suspicious of anyone who claims they do. To avoid confusion, we'll keep it simple. For this article, you can consider machine learning the process of finding patterns in data to understand something more or to predict some kind of future event.

Conceptually, AI is to the 2020s what DNA was to the 1990s, what bandwidth was to the early aughts, and mRNA was to the pandemic. You can't ignore the power of artificial intelligence because it's part of everyday life now. AI is designed to perform typical tasks involving some degree of problem solving and decision making that humans would normally do. Those tasks now range from making decisions regarding an insurance claim all the way to creating images from scratch based on text prompts. Many new uses of artificial intelligence, the technology, are still being discovered. Yet, if you think about the evolution of services like Siri or Alexa in our everyday lives, it's here too.