The paper introduces an Insurance Reserve Intelligence Platform that blends a classical Thiele-equation solver with a Physics-Informed Neural Network (PINN) enhanced by Knowledge-Informed Neural Network (KINN) losses for term-life reserve modeling. It generates synthetic policies, calculates risk-adjusted premiums, and constructs reserve-ratio datasets, then trains a neural model using seven features to predict standardized reserve ratios, achieving high accuracy (R² = 0.9887) and a 119.53× speedup over the classical solver. The framework also supports sensitivity analysis, elasticity analysis, prototype optimization, and interest-rate scenario testing, while noting remaining challenges in monotonicity and out-of-distribution generalization.
arXiv:2607. 04278v1 Announce Type: cross Abstract: We propose the first deep learning algorithm, the Certainty Equivalent Learning (CEL) algorithm, for solving high-dimensional discrete-time dynamic programming problems with recursive utility.
arXiv:2608. 04305v1 Announce Type: new Abstract: Risk-aware Q-learning (RaQL) provides a model-free, two-timescale estimator for dynamic risk objectives, but its finite-budget behavior remains fragile: fixed inner-loop hyperparameters can produce unstable value estimates, persistent Bellman residuals, and inefficient sample reuse.
arXiv:2606. 04420v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of ODEs and PDEs by minimising a weighted combination of residual, boundary, initial, and data losses.
Artificial Intelligence now underpins investment workflows from data and prediction to execution and tool use, yet its technical prowess does not automatically translate into profitability. A comprehensive review of public research up to 31 August 2026 across equities, ETFs, crypto spot, perpetual futures, and on‑chain markets shows real progress in prediction, text processing, portfolio design, and workflow integration, but evidence for durable net performance remains thin. The study highlights that factors such as temporal contamination, survivorship bias, weak benchmarks, implementation costs, and venue mechanics can erode alpha, and no single AI architecture has proven to deliver persistent, cross‑regime, capacity‑aware net alpha. "whyItMatters":"The findings underscore that while AI advances are evident, investors must rigorously test and govern AI systems to avoid overestimating their profitability potential."
arXiv:2605.28853v2 Announce Type: replace-cross Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction cos...
arXiv:2512. 14967v2 Announce Type: replace Abstract: We present a novel numerical method for solving McKean--Vlasov forward--backward stochastic differential equations (MV--FBSDEs) with common noise, combining Picard iterations, elicitability and deep learning.
arXiv:2607. 19377v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving partial differential equations, but their training is often affected by loss imbalance, optimization stiffness, and difficulty in capturing localized or multi-mode solution structures.
Physics-Informed Error Field Learning (PIEFL) is a post‑training optimization framework for Physics‑Informed Neural Networks (PINNs). After a primary network reaches satisfactory accuracy, PIEFL introduces an auxiliary error network that learns the discrepancy between the current approximation and the exact solution by deriving error control equations under physical constraints. The learned error correction is then combined with the primary prediction, improving solution accuracy without modifying the primary network architecture and focusing computational resources on correcting existing prediction errors.
arXiv:2606. 06823v1 Announce Type: cross Abstract: While deep learning has excelled in various domains, its application to sequential decision-making in finance remains challenging due to the low Signal-to-Noise Ratio (SNR) and non-stationarity of financial data.
The paper examines the reliability of data‑driven models for real‑time optimization (RTO) using a vinyl acetate monomer benchmark. Two models—a structured hybrid model and a fully data‑driven neural ODE—accurately reproduce plant measurements but yield economic optima that differ markedly from the plant’s true optimum, producing multiple phantom optima. The study shows that even with noise‑free data and correct initialization, stochastic gradient training can drift to weights that degrade RTO performance, indicating that predictive accuracy alone does not ensure reliable economic outcomes.
arXiv:2412. 11257v4 Announce Type: replace-cross Abstract: For many complex simulation tasks spanning areas such as healthcare, engineering, and finance, Monte Carlo (MC) methods are invaluable due to their unbiased estimates and precise error quantification.
arXiv:2606. 27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining.