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(Utolsó módosítás: 2015.VII.19.14:08 - MIAÚ-RSS)

FINANCE PROCESS AUTOMATION WITH NUMBERICAL DECISION MAKING (International Congress of Finance and Tax / March 10-11, 2023/ Konya, Türkiye)

Vezércikk: 2023. June (MIAU No. 298.)
(Előző cikk: MIAU No. 297.)

Keywords: artificial intelligence, anti-discrimination, similarity analysis

Abstract: The article will provide an in-depth discussion (*) on the advantages of numerical decision making in banking transactions, as well as its differences with binary (logical rule-based) decision making. It will highlight the importance (*) of using numerical decision making in today's digital age, and how (*) it can improve the overall efficiency (*), security (*), and transparency (*) of banking operations. Numerical decision making is based on data-driven analysis, and it allows banks to make informed (*) decisions, reducing (*) the risk of fraud. It enables banks to continuously improve (*) and optimize (*) their transaction processes and better (*) manage risks. On the other hand, binary decision making is a simplified approach of decision making that only considers two options in general, such as yes or no, pass or fail – like in the classic mathematical logic. Numeric decision produce fuzzy-like and/or quantum-like interpretation possibilities. Numerical decision-making is based on the application of mathematical and statistical models (more and more AI-approaches: like similarity analyses), which allows banks to determine the best (*) decision based on data. With the help of these models, banks can determine the probability (*) of transactions being related to fraud and thus determine the necessary (*) compliance in time. The article will explain how (*) numerical decision making can be built and how (*) it can help banks better (*) regulate the control mechanisms for transactions and increase (*) financial transparency in an automated way. By using data-driven decision making, banks can have better (*) oversight of transaction visibility and easier (*) enforcement of regulations. This method also allows banks to better (*) understand their customers' needs and personalize (*) their services more effectively, thereby increasing (*) financial engagement. In conclusion, the article will show that numerical decision making is crucial (*) for banks to stay ahead in a rapidly changing financial landscape. It provides a more comprehensive (*) and accurate (*) approach to decision making compared to binary decision making, and helps banks improve (*) their overall operations. The keywords with (*) signs will explain in the full-text-version and in the oral presentation through further details. Asterisks have been set where proofs and/or benchmarking processes should be presented in future. The numerical decision-making works as follows based on similarity analyses (https://miau.my-x.hu/myx-free/): … Demo: https://miau.my-x.hu/miau/296/risk_index_naive_regression_coco.xlsx (Tovább - DOC) *** (Tovább - PDF)

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