Many organizations depend on human decision-makers to make subjective decisions, especially in settings where information is scarce. Although workers are often viewed as interchangeable, the specific individual assigned to a task can significantly impact outcomes due to their unique decision-making processes and risk tolerance. In this paper, we introduce a novel framework that utilizes machine learning to predict worker behavior and employs integer optimization to strategically assign workers to tasks. Unlike traditional methods that treat machine learning predictions as static inputs for optimization, in our approach, the optimal predictive model used to represent a worker's behavior is determined by how that worker is allocated within the optimization process. We present a decision-aware optimization framework that integrates predictive model selection with worker allocation. Collaborating with an auto-insurance provider and using real-world data, we evaluate the effectiveness of our proposed method by applying three different techniques to predict worker behavior. Our findings show the proposed decision-aware framework outperforms traditional methods and offers context-sensitive and data-responsive strategies for workforce management.

Automotive insurers increasingly have access to telematic information via black-box recorders installed in the insured vehicle, and wish to identify undesirable behaviour which may signify increased risk or uninsured activities. However, identification of such behaviour with machine learning is non-trivial, and results are far from perfect, requiring human investigation to verify suspected cases. An appropriately formed priority score, generated by automated analysis of GPS data, allows underwriters to make more efficient use of their time, improving detection of the behaviour under investigation. An example of such behaviour is the use of a privately insured vehicle for commercial purposes, such as delivering meals and parcels. We first make use of trip GPS and accelerometer data, augmented by geospatial information, to train an imperfect classifier for delivery driving on a per-trip basis. We make use of a mixture of Beta-Binomial distributions to model the propensity of a policyholder to undertake trips which result in a positive classification as being drawn from either a rare high-scoring or common low-scoring group, and learn the parameters of this model using MCMC. This model provides us with a posterior probability that any policyholder will be a regular generator of automated alerts given any number of trips and alerts. This posterior probability is converted to a priority score, which was used to select the most valuable candidates for manual investigation. Testing over a 1-year period ranked policyholders by likelihood of commercial driving activity on a weekly basis. The top 0.9% have been reviewed at least once by the underwriters at the time of writing, and of those 99.4% have been confirmed as correctly identified, showing the approach has achieved a significant improvement in efficiency of human resource allocation compared to manual searching.

We all know data runs the world. The question is, can you align insurance with data? Data has always been at the heart of insurance. Although the modern commercial insurance industry may have begun with premiums calculated over a cup of coffee, it has now embraced a long list of more sophisticated analytical techniques, ranging from statistics to generalized linear models. AI in underwriting is the new shiny object in town. Let's cover if there's any merit to the hype. AI/ML can help uncover new insights from previously underutilized data, including unstructured data like text, speech, and images. It allows for using additional data during underwriting that would otherwise be unavailable or very difficult to obtain. Throughout this article, we will understand the power of AI in insurance underwriting, the benefits of underwriting automation, the future of underwriting, and everything in between.

Historically, underwriters have struggled to keep up with the vast and rapidly changing data landscape when evaluating property risks. This has often resulted in missed critical data points and less-than-optimal underwriting decisions. But with today's advancements in Artificial Intelligence and Machine Learning technology, insurers can now harness a wide range of data sources and analyze them with objective insights and recommendations. The end result: improved risk management. In the Fall '22 Release, Majesco is introducing Property Intelligence, a cutting-edge AI solution that empowers insurance carriers to make informed decisions about property risks.

Intel self-driving car unit Mobileye Global priced its initial public offering at $21 a share, a dollar above the top of its targeted range, according to people close to the deal. Mobileye raised $861 million by selling 41 million shares, valuing the company at roughly $17 billion, the people said. That is more than the $15.3 billion that Intel paid for the Mobileye in 2017 but a far cry from the $50 billion or more that the chip giant originally set its sights on when it unveiled plans for the listing late last year. The Wall Street Journal previously reported that Intel was expected to price the IPO at or above the top of its targeted range of $18 to $20 a share. A weekly digest of tech reviews, headlines, columns and your questions answered by WSJ's Personal Tech gurus.

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.

We're united by a mission: to make the world a safer place. Corvus Insurance uses novel data and artificial intelligence/machine learning to achieve better insights into commercial insurance risk. Our software empowers brokers and policyholders to better predict and prevent complex claims through data-driven tools and Smart Commercial Insurance policies. This allows us to reduce or eliminate the impact of adverse events, creating a safer world for everyone. Drawing inspiration from the intelligent, tool-building corvid family of birds, we are a team of high-flying collaborative builders.

Data has the ability to transform an insurance organization through meaningful insights. Sometimes these words, though, don't do justice to how much data can teach us and lead us toward greater profitability. Let's think of data in terms of "talent" for a moment. Imagine that you have been placed in charge of hiring someone who will lead your organization into the future with a whole new approach. Your supervisor gave you just one instruction.

The insurance industry has always dealt in data, but it hasn't always been able to put that data to optimal use. With the rise of artificial intelligence, which analyzes and learns from massive sets of digital information culled from public and private sources, insurers are embracing the technology's many facets -- from machine learning and natural language processing to robotic process automation and audio/video analysis -- to provide better products. Customers, too, are benefitting from practices like comparative shopping, quick claims processing, around-the-clock service and improved decision management. To get a better sense of how AI impacts the insurance industry, check out these 25 AI insurance applications. Liberty Mutual explores AI through its initiative Solaria Labs, which experiments in areas like computer vision and natural language processing. Auto Damage Estimator is one result of these efforts.

Will you be content if you hire and pay four people but only three show up to actually do the work? And yet, many employers do just that. According to McKinsey, an average worker spends 1.8 hours daily gathering and aggregating data, a task that is redundant and doesn't have a direct impact on business success, and, above all, can be easily automated. Is your insurance company looking for ways to relieve your employees from this routine burden while cutting costs and minimizing errors? If so, you can consult a robotic process automation company to build or customize an RPA solution specific to your needs.