Their novel solutions outperformed GPT -4, showcasing FinBen's potential to drive innovations in financial LLMs.

Roughly five years ago, Ant Group reined in its ambitions after a derailed initial public offering. Today, the Jack Ma-backed company is betting on a very different business to fuel its next phase of growth: health care powered by artificial intelligence. What began as a digital payments platform has become one of China's biggest investors in medical AI, backing software that fields patient questions and connects them with doctors, pharmacies and insurers. In November, Ant elevated its health unit to the same level as operations including Alipay and its lending businesses, underscoring how central the effort has become to the company's strategy. After years focused on consumer lending, wealth management and insurance technology, health care is now where executives believe AI can unlock the next wave of growth, leveraging Ant's massive user base to become its biggest business outside of payments.

Fairness is a central pillar of trustworthy machine learning, especially in domains where accuracy- or profit-driven optimization is insufficient. While most fairness research focuses on supervised learning, fairness in policy learning remains less explored. Because policy learning is interventional, it induces two distinct fairness targets: action fairness (equitable action assignments) and outcome fairness (equitable downstream consequences). Crucially, equalizing actions does not generally equalize outcomes when groups face different constraints or respond differently to the same action. We propose a novel double fairness learning (DFL) framework that explicitly manages the trade-off among three objectives: action fairness, outcome fairness, and value maximization. We integrate fairness directly into a multi-objective optimization problem for policy learning and employ a lexicographic weighted Tchebyshev method that recovers Pareto solutions beyond convex settings, with theoretical guarantees on the regret bounds. Our framework is flexible and accommodates various commonly used fairness notions. Extensive simulations demonstrate improved performance relative to competing methods. In applications to a motor third-party liability insurance dataset and an entrepreneurship training dataset, DFL substantially improves both action and outcome fairness while incurring only a modest reduction in overall value.

We propose a federated learning framework for the calibration of parametric insurance indices under heterogeneous renewable energy production losses. Producers locally model their losses using Tweedie generalized linear models and private data, while a common index is learned through federated optimization without sharing raw observations. The approach accommodates heterogeneity in variance and link functions and directly minimizes a global deviance objective in a distributed setting. We implement and compare FedAvg, FedProx and FedOpt, and benchmark them against an existing approximation-based aggregation method. An empirical application to solar power production in Germany shows that federated learning recovers comparable index coefficients under moderate heterogeneity, while providing a more general and scalable framework.

Apple's Siri AI will be powered by Gemini The automaker sold driver data to insurance companies without permission. After reaching a proposed settlement last year, the FTC has banned General Motors from sharing specific consumer data with third parties, reported. The finalized order wraps up one of the more egregious cases of a corporation collecting its customers' data and then using it against them. Two years ago, the released a report detailing how GM's OnStar Smart Driver program collected and sold detailed geolocation and driving behavior data to third parties, including data brokers. Those brokers in turn sold the data to insurance providers, which jacked up the rates for some drivers based on the data.