"Intelligent" finance and treasury management: what we can expect

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F19%3A10243528" target="_blank" >RIV/61989100:27510/19:10243528 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43110/20:43916628

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s00146-019-00919-6" target="_blank" >https://link.springer.com/article/10.1007/s00146-019-00919-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00146-019-00919-6" target="_blank" >10.1007/s00146-019-00919-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

"Intelligent" finance and treasury management: what we can expect

Popis výsledku v původním jazyce

Artificial intelligence poses a particular challenge in its application to finance/treasury management because most treasury functions are no longer physical processes, but rather virtual processes that are increasingly highly automated. Most finance/treasury teams are knowledge workers who make decisions and conduct analytics within often dynamic frameworks that must incorporate environmental considerations (foreign exchange rates, GDP forecasts), internal considerations (growth needs, business trends), as well as the impact of any actions on related corporate decisions which are also highly complex (e.g., hedging, investing, capital structure, liquidity levels). Artificial intelligence in finance and treasury is thus most analogous to the complexity of a human nervous system as it encompasses far more than the automation of tasks. Similar to the human nervous system, AI systems in finance/treasury must manage data quickly and accurately, including the capture and classification of data and its integration into larger datasets. At present, the AI network neural system has been gradually improved and is widely used in many fields of treasury management, such as early warning of potential financial crisis, diagnosis of financial risk, control of financial information data quality and mining of hidden financial data, information, etc. (C) 2019, Springer-Verlag London Ltd., part of Springer Nature.

Název v anglickém jazyce

"Intelligent" finance and treasury management: what we can expect

Popis výsledku anglicky

Artificial intelligence poses a particular challenge in its application to finance/treasury management because most treasury functions are no longer physical processes, but rather virtual processes that are increasingly highly automated. Most finance/treasury teams are knowledge workers who make decisions and conduct analytics within often dynamic frameworks that must incorporate environmental considerations (foreign exchange rates, GDP forecasts), internal considerations (growth needs, business trends), as well as the impact of any actions on related corporate decisions which are also highly complex (e.g., hedging, investing, capital structure, liquidity levels). Artificial intelligence in finance and treasury is thus most analogous to the complexity of a human nervous system as it encompasses far more than the automation of tasks. Similar to the human nervous system, AI systems in finance/treasury must manage data quickly and accurately, including the capture and classification of data and its integration into larger datasets. At present, the AI network neural system has been gradually improved and is widely used in many fields of treasury management, such as early warning of potential financial crisis, diagnosis of financial risk, control of financial information data quality and mining of hidden financial data, information, etc. (C) 2019, Springer-Verlag London Ltd., part of Springer Nature.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

AI &amp; Society

ISSN

0951-5666

e-ISSN

—

Svazek periodika

neuveden

Číslo periodika v rámci svazku

11.10.2019

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

—

Kód UT WoS článku

000490035700001

EID výsledku v databázi Scopus

2-s2.0-85074460377