Towards Explainable and Reliable AI in Finance
Albi Isufaj, Pablo Mollá, Helmut Prendinger
TL;DR
This paper addresses the opacity of neural forecasts in finance and the need for explainability under regulatory regimes. It proposes an integrated framework combining Time-LLM with a Prompt-as-Prefix to guide forecasts, a Corrective AI meta-labeling reliability estimator to filter predictions, and a knowledge-based symbolic reasoning layer for transparent justification. Experiments on equity and cryptocurrency time series show reductions in false positives and enable selective execution while preserving performance. By fusing predictive accuracy with reliability assessment and rule-based reasoning, the approach offers a transparent, auditable pathway for deploying financial AI in high-stakes settings.
Abstract
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.