Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance
Practitioners and researchers trying to strike a balance between accuracy and transparency center Explainable Artificial Intelligence (XAI) at the junction of finance. This paper offers a thorough overview of the changing scene of XAI applications in finance together with domain-specific implementations, methodological developments, and trend mapping of research. Using bibliometric and content analysis, we find topic clusters, significant research, and most often used explainability strategies used in financial industries. Our results show a substantial dependence on post-hoc interpretability techniques; attention mechanisms, feature importance analysis and SHAP are the most often used techniques among them. This review stresses the need of multidisciplinary approaches combining financial knowledge with improved explainability paradigms and exposes important shortcomings in present XAI systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Feature ImportanceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Voting Approach for Explainable Classification with Rule Learning
State-of-the-art results in typical classification tasks are mostly achieved by unexplainable machine learning methods, like deep neural networks, for instance. Contrarily, in this paper, we investigate the application o…
ClassificationExplainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review
With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-…
Breast Cancer DetectionExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Fairness+2An AI Architecture with the Capability to Explain Recognition Results
Explainability is needed to establish confidence in machine learning results. Some explainable methods take a post hoc approach to explain the weights of machine learning models, others highlight areas of the input contr…
Explaining AI in Finance: Past, Present, Prospects
This paper explores the journey of AI in finance, with a particular focus on the crucial role and potential of Explainable AI (XAI). We trace AI's evolution from early statistical methods to sophisticated machine learnin…
Decision MakingExplainable Artificial Intelligence (XAI)regressionA Comprehensive Review on Financial Explainable AI
The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex p…
Decision MakingDeep Learning