Background The most popular statistical model used in modeling general insurance claims is the Generalized Linear Model (GLM), introduced by Nelder and Wedderburn (1972). GLMs allow actuaries to incorporate a wide range of statistical distributions that are commonly adopted in actuarial analytics, and the underlying linearity assumption provides an explainable framework for claims modeling (Wüthrich and Merz, 2023). This model has been shown to work well in practice; however, deep learning--the subset of machine learning focusing on artificial neural network models--has been gaining substantial ground in recent years. Applications of deep learning and other novel machine learning techniques in claim modeling have shown an improvement in prediction accuracy compared to classical methods such as the GLM (Noll et al., 2020; Wüthrich and Buser, 2023). Nevertheless, the integration of such advanced techniques as the primary pricing method among actuaries has been slow since they are often perceived as "black boxes", where the intricacies of the inner workings remain obscured, making it challenging to decipher the rationale behind the models' predictions (Harris et al., 2024). Corresponding author Email address: tupho289@gmail.com

Deep neural networks (DNNs) have garnered significant attention in financial asset pricing, due to their strong capacity for modeling complex nonlinear relationships within financial data. However, sophisticated models are prone to over-fitting to the noise information in financial data, resulting in inferior performance. To address this issue, we propose an information bottleneck asset pricing model that compresses data with low signal-to-noise ratios to eliminate redundant information and retain the critical information for asset pricing. Our model imposes constraints of mutual information during the nonlinear mapping process. Specifically, we progressively reduce the mutual information between the input data and the compressed representation while increasing the mutual information between the compressed representation and the output prediction. The design ensures that irrelevant information, which is essentially the noise in the data, is forgotten during the modeling of financial nonlinear relationships without affecting the final asset pricing. By leveraging the constraints of the Information bottleneck, our model not only harnesses the nonlinear modeling capabilities of deep networks to capture the intricate relationships within financial data but also ensures that noise information is filtered out during the information compression process.

Option pricing models, essential in financial mathematics and risk management, have been extensively studied and recently advanced by AI methodologies. However, American option pricing remains challenging due to the complexity of determining optimal exercise times and modeling non-linear payoffs resulting from stochastic paths. Moreover, the prevalent use of the Black-Scholes formula in hybrid models fails to accurately capture the discontinuity in the price process, limiting model performance, especially under scarce data conditions. To address these issues, this study presents a comprehensive framework for American option pricing consisting of six interrelated modules, which combine nonlinear optimization algorithms, analytical and numerical models, and neural networks to improve pricing performance. Additionally, to handle the scarce data challenge, this framework integrates the transfer learning through numerical data augmentation and a physically constrained, jump diffusion process-informed neural network to capture the leptokurtosis of the log return distribution. To increase training efficiency, a warm-up period using Bayesian optimization is designed to provide optimal data loss and physical loss coefficients. Experimental results of six case studies demonstrate the accuracy, convergence, physical effectiveness, and generalization of the framework. Moreover, the proposed model shows superior performance in pricing deep out-of-the-money options. Introduction Options are fundamental financial derivatives widely employed for risk management. The movement of option prices follows a stochastic process influenced by various factors such as the price process of the underlying assets ( S t), the strike price (K), the time-to-maturity ( T), the option type (American or European; Put ( P) or Call ( C) options), and numerous macroeconomic and market factors.

Incumbents like CBA are increasingly looking to A.I. technology to solve their business problems and are eyeing external tech partners to source those A.I. solutions. But these traditional companies have faced challenges nurturing meaningful collaborations that maximize the support they get from A.I. players. Only 1 in 5 incumbents found the right kind of A.I. player, like H20.ai is for CBA, that offers access to custom technology, as well as support for talent, training, and change management, prompting the incumbent to overhaul its processes. We call these A.I. players that provide such support transformers. For industry incumbents that are able to identify and effectively collaborate with a transformer, the value is clear.

At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher premium than non-risky or less-risky ones. Actuaries, therefore, use econometric or machine learning techniques to classify, but the distinction between a fair actuarial classification and "discrimination" is subtle. For this reason, there is a growing interest about fairness and discrimination in the actuarial community Lindholm, Richman, Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can serve as substitutes or proxies for protected attributes. For example, the color and model of a car, combined with the driver's occupation, may lead to an undesirable gender bias in the prediction of car insurance prices. Surprisingly, we will show that debiasing the predictor alone may be insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing model is currently built in a two-stage structure that considers many potentially biased components such as car or geographic risks. We will show that this traditional structure has significant limitations in achieving fairness. For this reason, we have developed a novel pricing model approach. Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau (2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.

AI is a hot commodity in the marketing space – but like all new tools, many companies adopting it don't fully understand its benefits and its drawbacks. As a result, most marketers are using AI in an ad hoc manner, lacking in a clear strategy. To really gain a competitive advantage, executives need to rethink the scope and framing of how they are viewing AI and shift from improving on traditional and familiar marketing strategies to considering game-changing new ideas. For today's exercise, consider these questions and ask yourself where AI could assist in making critical strategic decisions. As a marketer, how are you looking for new markets or product segments?

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Everyone knows the pain that follows after your CFO takes a look at the cloud computing bill. The products and services are priced in fractions of a cent, but somehow those fractions all add up. The good news is that more options are appearing as smaller clouds compete directly on price. In most cases, it's not quite accurate to use the word MB Because these cloud competitors are often quite big--they just don't have the massive size and visibility of the major clouds. That's why some prefer to use the word independent.

Upshot, a blockchain-based protocol providing industry-leading non-fungible token (NFT) appraisals, announced the hiring of Orestis Tsinalis as Head of Data & Machine Learning. Tsinalis will report directly to CEO Nick Emmons and will be responsible for leading Upshot's growing data and machine learning teams, as well as improving the company's ML models to deliver real-time NFT appraisals for the market. Prior to joining Upshot, Tsinalis served as Head of Quantitative Research and as a Quantitative Trader at GHCO – one of the fastest-growing liquidity providers specializing in ETFs. While at GHCO, Tsinalis built and scaled pricing models for the company's global ETF book, developed new strategies for trading crypto ETPs, coins and futures, and built the ML infrastructure for backtesting high-frequency trading strategies. Tsinalis also served as the Principal Data Scientist at abrdn, a top-five UK asset manager with over $600b in AUM.

How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.