arXiv AI By Lasse Dierich, Orestis Schinas

Artificial Intelligence in Ship Finance: Applications, Opportunities, and a Case Study in AI-Augmented Loan Origination

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arXiv:2606. 11238v1 Announce Type: cross Abstract: Ship finance is a data-intensive and document-heavy segment of asset-based lending, requiring the integration of financial, technical, contractual, and regulatory information from heterogeneous and largely unstructured sources.

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arXiv AI
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ASMR: Agentic Schema Generation for Ship Maintenance Report Writing

arXiv:2607. 08177v1 Announce Type: new Abstract: In this paper, we study the automatic schema generation problem: given a collection of historical ship maintenance and operational reports across multiple form categories, automatically discover compact and informative schemas that capture the essential information requirements of each report type.

By Sohrab Namazi Nia, Amogh Dalal, Ning Sa, Peter Ly, Marti Zentmaier, Tomek Strzalkowski, Jay Miller, Rishi Singh, Senjuti Basu Roy
arXiv Machine Learning
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AI Economist Agent: An Agentic Framework for Evidence-Based Economic and Financial Analysis with RAG, Knowledge Graphs, and Large Language Models

The paper introduces an AI economist agent that integrates large language models, retrieval‑augmented generation, knowledge graphs, and quantitative models to conduct evidence‑based economic and financial scenario analysis. The framework orchestrates LLM agents to plan analyses, retrieve relevant evidence, and structure economic mechanisms, while registered quantitative models produce numerical outcomes and predefined tests validate intermediate results for inclusion in the final report. Applied to European macro‑financial stress scenarios and bank capital analysis, the empirical study demonstrates the agent’s ability to combine flexible evidence retrieval and scenario construction while maintaining traceability to sources and explicit model calculations.

By Masahiro Kato