Towards Explainable and Reliable AI in Finance

TL;DR

This paper proposes methods to enhance the explainability and reliability of AI models in finance by combining foundation models, reliability estimators, and symbolic reasoning, leading to more trustworthy financial forecasts.

Contribution

It introduces a framework integrating foundation models, reliability filtering, and symbolic reasoning to improve transparency and trustworthiness in financial AI systems.

Findings

Reduces false positives in financial predictions

Supports selective execution of reliable forecasts

Enhances transparency through rule-based reasoning

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.