A Comprehensive Review on Financial Explainable AI
Comprehensive review of explainable AI methods in finance, focusing on transparency, challenges, and future directions.
What it examines
This survey reviews methods to improve the explainability of deep learning models in finance, categorizing explainable AI methods and addressing challenges and future directions. It aims to enhance transparency in financial decision-making processes.
What it concludes
The study suggests that XAI techniques can enhance transparency and trust in financial AI models. Future research should focus on improving transparent models and integrating NLP for better explanations. Potential applications include credit scoring, fraud detection, and investment management.
Evidence objects
The study suggests that XAI techniques can enhance transparency and trust in financial AI models. Future research should focus on improving transparent models and integrating NLP for better explanations. Potential applications include credit scoring, fraud detection, and investment management.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: 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 patterns. However, due to their lack of explainability, there are significant concerns regarding their use in critical sectors, such as finance and healthcare, where decision-making… ▽ More 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 patterns. However, due to their lack of explainability, there are significant concerns regarding their use in critical sectors, such as finance and healthcare, where decision-making transparency is of paramount importance. In this paper, we provide a comparative survey of methods that aim to improve the explainability of deep learning models within the context of finance. We categorize the collection of explainable AI methods according to their corresponding characteristics, and we review the concerns and challenges of adopting explainable AI methods, together with future directions we deemed appropriate and important. △ Less
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