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Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

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Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed? / Kruse, Luisa; Wunderlich, Nico; Beck, Roman.

In: Proceedings of the Annual Hawaii International Conference on System Sciences, 2019, p. 6408-6417.

Research output: Journal Article or Conference Article in JournalConference articleResearchpeer-review

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@inproceedings{77d2e556b99e48c8bd4cc30283b16ea5,
title = "Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?",
abstract = " As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns.",
author = "Luisa Kruse and Nico Wunderlich and Roman Beck",
note = "ISBN: 978-0-9981331-2-6",
year = "2019",
doi = "http://hdl.handle.net/10125/60075",
language = "English",
pages = "6408--6417",
journal = "Proceedings of the Annual Hawaii International Conference on System Sciences",
issn = "1060-3425",
publisher = "IEEE Computer Society Press",

}

RIS

TY - GEN

T1 - Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

AU - Kruse, Luisa

AU - Wunderlich, Nico

AU - Beck, Roman

N1 - ISBN: 978-0-9981331-2-6

PY - 2019

Y1 - 2019

N2 - As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns.

AB - As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns.

U2 - http://hdl.handle.net/10125/60075

DO - http://hdl.handle.net/10125/60075

M3 - Conference article

SP - 6408

EP - 6417

JO - Proceedings of the Annual Hawaii International Conference on System Sciences

JF - Proceedings of the Annual Hawaii International Conference on System Sciences

SN - 1060-3425

ER -

ID: 83381739