发表机构
zeb.rolfes.schierenbeck.associates Ltd.; zeb Institute for Financial Services; University Witten/Herdecke（策布·罗尔费斯·希伦贝克联合公司; 策布金融服务学院; 维滕/赫尔德克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了结合计量模型与AI的多视角利率预测原型，经欧洲大型银行测试可精准灵活预测利率，整合多类方法助力银行资产负债管理决策，为银行风险管理提供显著价值。
AI 中文摘要
本研究致力于开发一种由人工智能支持的多视角利率预测原型，该原型将经典计量经济学模型与现代人工智能方法相结合。在一家欧洲大型银行进行测试后，该系统能够更精准、灵活地预测利率走势，为资产负债管理（ALM）领域的战略决策提供支撑。它在交互式平台内整合了主题建模、情感分析、计量经济学预测以及基于市场的分析。借助人工智能分析海量金融文档与市场数据，该系统可早期识别货币政策趋势与情感信号。核心计量经济学模型为贝叶斯向量自回归（BVAR）模型，支持基于模拟的情景分析，以从多个视角评估经济发展。该系统的创新之处在于整合了多种预测方法，这些方法整合了此前相互独立的信息源，并以透明、可解释的方式呈现。金融分析师与风险管理人员因此获得了更优的决策依据，能够更精准地评估利率风险，更主动地管理市场动态。尽管该原型展示了人工智能如何变革银行的利率管理，但仍需进一步开发以优化实时数据集成与监管合规性。即便处于当前阶段，本研究表明，多视角、人工智能驱动的预测通过提升透明度、强化循证决策、改善风险管理，为银行提供了显著的附加价值。
英文摘要
This study focuses on developing an AI-supported prototype for multiperspective interest rate forecasting that combines classical econometric models with modern artificial intel-ligence methods. Tested in a major European bank, the system enables more precise and flexible prediction of interest rate developments, supporting strategic decision-making in Asset-Liability Management (ALM). It integrates topic modeling, sentiment analysis, econometric forecasting, and market-based analyses within an interactive platform. Leveraging AI to analyze large volumes of financial documents and market data enables the identification of monetary policy trends and sentiment signals at an early stage. The core econometric model is a Bayesian vector autoregression (BVAR) that enables simulation-based scenario analyses to evaluate economic developments from multiple perspectives. The system's innovation lies in its integration of several forecasting approaches that consolidate previously separate information sources and present them transparently and interpretably. Financial analysts and risk managers thus gain a better basis for making decisions, allowing them to assess interest rate risks more accurately and manage market movements more proactively. While the prototype demonstrates how AI can transform interest rate management in banking, further development is required to optimize real-time data integration and regulatory compliance. Even at this stage, the study shows that multi-perspective, AI-driven forecasting provides substantial added value for banks by increasing transparency, strengthening evidence-based decision-making, and improving risk management.
