Machine learning (ML) and deep learning (DL) have gained significant popularity in various aspects of data science and has increasingly found its place in prediction tasks for financial and economic problem sets. While the integration of ML and DL techniques into financial prediction and forecasting tasks holds the promise of enhanced predictive accuracy, this advantage comes at the expense of increased complexity and reduced interpretability. Such complex methods are often characterized as “black-boxes” because comprehending how variables interact to produce a specific outcome can oftentimes be very challenging. The limited transparency impacts the reliability of ML and DL models and the willingness of industry experts to employ them in sensitive domains like finance.
Although many emerging XAI tools have an established utility in brining explainability to AI-based systems build in certain use cases, their applicability to complex models developed for financial data in general, and financial time series in particular, remains rather limited. To address this gap, we have developed a variety of solutions that aim at bringing forward explainability in AI-systems applied to financial problem sets. Specifically, we have developed an open-source platform where users can evaluate the trustworthiness of AI-based systems applied to two different use cases: credit risk estimation and financial time series forecasting.