AI-Driven Multiscenario Interest Rate Forecasting: A Proof of Concept for Banking Asset Management

This study focuses on the development of an AI-supported prototype for multiperspective interest rate forecasting, which combines classical econometric models with modern artificial intelligence methods. The system, which was tested in a major European bank, enables a more precise and flexible prediction of interest rate developments and supports strategic decision-making in Asset-Liability Management (ALM). The prototype integrates topic modelling, sentiment analysis, econometric forecasting, and market-based analyses within an interactive platform. By leveraging AI to analyze large volumes of financial documents and market data, monetary policy trends and sentiment signals can be identified at an early stage. The core econometric model, a Bayesian vector autoregression (BVAR), enables simulation-based scenario analyses that evaluate economic developments from multiple perspectives. The innovation of the system lies in the integration of several forecasting approaches that consolidate previously separate information sources and present them in a transparent and interpretable manner. Financial analysts and risk managers thus gain an improved decision-making basis, allowing for a more accurate assessment of interest rate risks and more forward-looking management of market movements. While the prototype already 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 steering.

Improvements to AI Systems:

1. Integrate a hybrid forecasting pipeline that combines Bayesian vector autoregression (BVAR) with transformer-based language models, allowing the AI to jointly process structured time-series data and unstructured financial text (e.g., central bank minutes, news) in a single latent representation.

2. Add a multi-perspective fusion layer that explicitly weights and reconciles outputs from topic modeling, sentiment analysis, econometric forecasts, and market-based signals, with uncertainty quantification for each source to prevent over-reliance on any single method.

3. Implement scenario-aware simulation modules that use the BVAR’s posterior distribution to generate counterfactual interest rate paths under user-defined shocks (e.g., policy rate changes, inflation spikes), enabling the AI to answer “what-if” questions with calibrated confidence intervals.

4. Develop an interpretable attention mechanism over financial documents that highlights key sentences, entities, and temporal events driving the forecast, so analysts can trace each prediction back to specific evidence.

5. Enable real-time adaptive recalibration of the BVAR parameters using online learning, so the system updates its forecasts as new market data and text streams arrive, without full retraining.

6. Add a regulatory compliance checker that flags forecasts or scenarios that violate internal risk limits or external reporting standards, and automatically generates audit trails for model decisions.

What the Improved AI System Can Do:

• Generate daily, multi-horizon interest rate forecasts that blend quantitative econometrics with qualitative sentiment, reducing prediction error compared to single-method baselines.

• Provide analysts with a dashboard showing why a forecast changed (e.g., “sentiment shifted negative on ECB communication”) and how much each data source contributed.

• Run stress tests on demand, such as “simulate a 200bp rate hike with a recession,” and output probability distributions for ALM metrics like net interest income or duration gap.

• Automatically draft narrative summaries of forecast changes for risk committee meetings, including caveats and confidence levels.

• Detect early shifts in monetary policy stance by clustering topic evolutions and sentiment anomalies across thousands of documents, days before traditional indicators react.

• Maintain full explainability and auditability, satisfying internal model risk management and external supervisory expectations (e.g., ECB’s SSM guidelines).

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.