Climate change in recent years has produced viable shipping routes in the Arctic. However, critical uncertainties related to maritime operations in the Arctic make it difficult to predict ship speeds in ice and, thus, the voyage time and fuel costs. Cost–benefit analysis of alternative Arctic routes based on accurate environmental condition modeling is required. In this context, this paper presents a holistic approach that considers the major voyage-related costs of a trans-Arctic route as an alternative to the conventional routes via the Suez Canal Route (SCR) for existing merchant ships. This tool is based on high-fidelity models of ship performance, metocean forecasting, and a voyage optimization algorithm. Case studies are performed based on a general cargo vessel in operation to quantify realistic expenses inclusive of all the major operational, fuel, and voyage costs of the specific voyages. A comparison is made between the total costs of the trans-Arctic route and SCR for different seasons, which proves the economic feasibility of the trans-Arctic route. Overall, this work can provide valuable insights to help policymakers as well as shipbuilders, owners, and operators to assess the potential cost-effectiveness and sustainability of future Arctic shipping, thereby better developing future strategies.

Recent advances in large language models, tool-using agents, and financial machine learning are shifting financial automation from isolated prediction tasks to integrated decision systems that can perceive information, reason over objectives, and generate or execute actions. The paper develops an integrative framework for analysing agentic finance: financial market environments in which autonomous or semi-autonomous AI systems participate in information processing, decision support, monitoring, and execution workflows. The analysis proceeds in three steps. First, the paper proposes a four-layer architecture of financial AI agents covering data perception, reasoning engines, strategy generation, and execution with control. Second, it introduces the Agentic Financial Market Model (AFMM), a stylised agent-based representation linking agent design parameters such as autonomy depth, heterogeneity, execution coupling, infrastructure concentration, and supervisory observability to market-level outcomes including efficiency, liquidity resilience, volatility, and systemic risk. Third, it presents an illustrative empirical application based on event studies of AI-agent capability disclosures and heterogeneous market repricing. It argues that the systemic implications of AI in finance depend less on model intelligence alone than on how agent architectures are distributed, coupled, and governed across institutions. The empirical application is intentionally exploratory: it does not validate the full AFMM but shows how one observable expectations channel can be studied using public data. In the near term, the most plausible equilibrium is bounded autonomy, in which AI agents operate as supervised co-pilots, monitoring systems, and constrained execution modules embedded within human decision processes.

Artificial intelligence (AI) agents can propose financial transactions, but model output should not itself grant execution authority. We develop Authority–Inference Separation (AIS), in which an independent control plane evaluates a typed financial action intent (FAI) against institutional mandate, policy, current state, human approvals, and exact economic terms before issuing single-use authority. We specify the threat and control boundaries, instantiate AIS across four financial domains, and test fixed fixtures, a separate external-policy-gateway baseline, mechanism ablations, two small open-weight models, and an Ethereum Virtual Machine (EVM) contract. In 36 fixed attack fixtures, AIS accepted no unauthorized effect, compared with 36 for direct execution and 20 for prompt policy. A gateway without execution-bound authorization accepted all 20 targeted post-decision attacks. Both models followed all eight injected counterparty substitutions; the independent gate prevented unsafe execution but authorized only six of 16 legitimate requests, exposing an availability cost. The EVM prototype rejected replay, revocation, stale-policy, and call-mutation attempts. These are conformance tests, not field estimates. AIS combines established security mechanisms around financial authority and replayable evidence. A ledger can enforce granted authority and record settlement, while mandate legitimacy, delivery, and accounting require complementary governance and evidence.