Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature

Weber, Patrick; Carl, Kim Valerie; Hinz, Oliver

doi:10.1007/s11301-023-00320-0

Cited by 62 publications

(15 citation statements)

References 111 publications

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“…The integration of LLMs [4] into financial planning is a relatively new and emerging field. Recent studies have begun to explore the capabilities of models like OpenAI's GPT series and explainable AI techniques in generating financial narratives [17,18]. Our work extends this line of research by embedding LLM insights into the optimization framework, a novel approach that has not been extensively covered in the existing literature.…”

Section: Related Workmentioning

confidence: 93%

Optimized Financial Planning: Integrating Individual and Cooperative Budgeting Models with LLM Recommendations

de Zarzà,

de Curtò,

Roig

et al. 2023

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In today’s complex economic environment, individuals and households alike grapple with the challenge of financial planning. This paper introduces novel methodologies for both individual and cooperative (household) financial budgeting. We firstly propose an optimization framework for individual budget allocation, aiming to maximize savings by efficiently distributing monthly income among various expense categories. We then extend this model to households, wherein the complexity of handling multiple incomes and shared expenses is addressed. The cooperative model prioritizes not only maximized savings but also the preferences and needs of each member, fostering a harmonious financial environment, whether they are short-term needs or long-term aspirations. A notable innovation in our approach is the integration of recommendations from a large language model (LLM). Given its vast training data and potent inferential capabilities, the LLM provides initial feasible solutions to our optimization problems, acting as a guiding beacon for individuals and households unfamiliar with the nuances of financial planning. Our preliminary results indicate that the LLM-recommended solutions result in budget plans that are both economically sound, meaning that they are consistent with established financial management principles and promote fiscal resilience and stability, and aligned with the financial goals and preferences of the concerned parties. This integration of AI-driven recommendations with econometric models, as an instantiation of an extended coevolutionary (EC) theory, paves the way for a new era in financial planning, making it more accessible and effective for a wider audience, as we propose an example of a new theory in economics where human behavior can be greatly influenced by AI agents.

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Section: Related Workmentioning

confidence: 93%

Optimized Financial Planning: Integrating Individual and Cooperative Budgeting Models with LLM Recommendations

de Zarzà,

de Curtò,

Roig

et al. 2023

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“…The authors identify a void in the approaches to process the multimodal data widely prevalent in healthcare applications, in an interpretable manner and call for collaboration with the sub-community in XAI research that deals with explaining Graph Neural Networks, capable of processing multimodal data [40]. Weber et al [7] review the application of XAI methods in finance systems and identify the skewedness in the distribution of leveragement of different XAI approaches. They call for applying techniques to extract causal explanations in a privacy-preserving manner, a crucial requirement that must be satisfied in finance.…”

Section: A Brief Overview Of the Previous Attempts In Explainable Aimentioning

confidence: 99%

“…Furthermore, Clement et al [8] present a comprehensive survey that positions various XAI methods with respect to software development principles. Researchers interested in applying XAI techniques to these application domains are encouraged to refer to these surveys [6,7,39,41,45], which provide detailed reviews of methods tailored to specific applications.…”

Section: A Brief Overview Of the Previous Attempts In Explainable Aimentioning

confidence: 99%

“…XAI aims to unravel the decision-making process of complex black box models in a human-interpretable manner [5]. By providing explanations, XAI techniques can enhance user trust and enable the adoption of opaque models in safety-critical domains such as healthcare [6] and finance [7], where transparency is essential.…”

Section: Introductionmentioning

confidence: 99%

“…This survey paper focuses on organizing XAI approaches that explain the working of Convolutional Neural Networks (CNNs), which are state-of-the-art models for image classification. While there exist various surveys in the literature [6][7][8][9][10] with different aims and scopes, our paper aims to provide a multi-view taxonomy of XAI approaches by carefully analyzing the existing literature. The taxonomy considers the incorporation of explainability during the training phase (antehoc) and approximating the black box's working mechanism without disturbing the deployed model (posthoc).…”

Section: Introductionmentioning

confidence: 99%

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Explainable Image Classification: The Journey So Far and the Road Ahead

Kamakshi,

Krishnan

2023

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Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address the interpretability challenges posed by complex machine learning models. In this survey paper, we provide a comprehensive analysis of existing approaches in the field of XAI, focusing on the tradeoff between model accuracy and interpretability. Motivated by the need to address this tradeoff, we conduct an extensive review of the literature, presenting a multi-view taxonomy that offers a new perspective on XAI methodologies. We analyze various sub-categories of XAI methods, considering their strengths, weaknesses, and practical challenges. Moreover, we explore causal relationships in model explanations and discuss approaches dedicated to explaining cross-domain classifiers. The latter is particularly important in scenarios where training and test data are sampled from different distributions. Drawing insights from our analysis, we propose future research directions, including exploring explainable allied learning paradigms, developing evaluation metrics for both traditionally trained and allied learning-based classifiers, and applying neural architectural search techniques to minimize the accuracy–interpretability tradeoff. This survey paper provides a comprehensive overview of the state-of-the-art in XAI, serving as a valuable resource for researchers and practitioners interested in understanding and advancing the field.

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