Towards Explainable and Reliable AI in Finance
This work addresses trust and regulatory compliance issues in finance by making AI systems more explainable and reliable, though it appears incremental as it combines existing approaches like foundation models and symbolic reasoning.
The paper tackles the problem of opaque neural network models in financial forecasting by proposing a framework that integrates predictive performance with reliability estimation and rule-based reasoning, resulting in reduced false positives and support for selective execution on equity and cryptocurrency data.
Financial forecasting increasingly uses large neural network models, but their opacity raises challenges for trust and regulatory compliance. We present several approaches to explainable and reliable AI in finance. \emph{First}, we describe how Time-LLM, a time series foundation model, uses a prompt to avoid a wrong directional forecast. \emph{Second}, we show that combining foundation models for time series forecasting with a reliability estimator can filter our unreliable predictions. \emph{Third}, we argue for symbolic reasoning encoding domain rules for transparent justification. These approaches shift emphasize executing only forecasts that are both reliable and explainable. Experiments on equity and cryptocurrency data show that the architecture reduces false positives and supports selective execution. By integrating predictive performance with reliability estimation and rule-based reasoning, our framework advances transparent and auditable financial AI systems.