A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection
This addresses the need for trust in AI systems in finance by improving explainability for time series data, representing an incremental advance in XAI methods.
The paper tackles the problem of explaining deep learning predictions for time series data, which existing XAI methods struggle with due to dependence and non-stationarity, by proposing a novel technique that preserves time ordering, with applications in risk-management and fraud detection.
Artificial intelligence is creating one of the biggest revolution across technology driven application fields. For the finance sector, it offers many opportunities for significant market innovation and yet broad adoption of AI systems heavily relies on our trust in their outputs. Trust in technology is enabled by understanding the rationale behind the predictions made. To this end, the concept of eXplainable AI emerged introducing a suite of techniques attempting to explain to users how complex models arrived at a certain decision. For cross-sectional data classical XAI approaches can lead to valuable insights about the models' inner workings, but these techniques generally cannot cope well with longitudinal data (time series) in the presence of dependence structure and non-stationarity. We here propose a novel XAI technique for deep learning methods which preserves and exploits the natural time ordering of the data.