Abstract
The influence of Artificial Intelligence is growing, as is the need to make it as explainable as possible. Explainability is one of the main obstacles that AI faces today on the way to more practical implementation. In practise, companies need to use models that balance interpretability and accuracy to make more effective decisions, especially in the field of finance. The main advantages of the multi-criteria decision-making principle (MCDM) in financial decision-making are the ability to structure complex evaluation tasks that allow for well-founded financial decisions, the application of quantitative and qualitative criteria in the analysis process, the possibility of transparency of evaluation and the introduction of improved, universal and practical academic methods to the financial decision-making process. This article presents a review and classification of multi-criteria decision-making methods that help to achieve the goal of forthcoming research: to create artificial intelligence-based methods that are explainable, transparent, and interpretable for most investment decision-makers.
Highlights
Artificial Intelligence (AI) has grown significantly in use and is becoming more standardised in the twenty-first century
The main purpose of this article is to present a review of multi-criteria decision-making (MCDM) methods that can contribute to achieving the goal of forthcoming research in creating artificial intelligence-based methods that are explainable, transparent, and interpretable for most investment decision-makers
There are many different models and validation methods available to aid in financial data mining and decision-making
Summary
Artificial Intelligence (AI) has grown significantly in use and is becoming more standardised in the twenty-first century. Artificial intelligence is increasingly applied in the financial industry and is likely to become more important in the coming years. Modern applications of AI in the financial sector are diverse and extensive, at both the front and back ends of business processes. Examples of modern artificial intelligence applications in finance include transaction data analysis, improved chatbots, identity checking during client registration, fraud detection in claims control, pricing in bond trading, anti-money laundering monitoring, price differentiation in auto insurance, automated analysis of legal articles, risk control, portfolio management, client relationship control, and execution of trade and investment transactions. Multi-criteria methods are widely used for decision-making in various commercial and financial contexts because of the diversity of solutions they can provide. Most scientists and practitioners apply the methods of multicriteria operations research when solving financial decision-making problems
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