| 000 -LEADER |
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| fixed length control field |
03898nam a22003497a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
00006677 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
ES-MaONT |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20211006062654.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
210825s2021 fr db||f t|||i00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
ES-MaONT |
| 245 00 - TITLE STATEMENT |
|---|
| Title |
Artificial Intelligence, Machine Learning and Big Data in Finance |
| Remainder of title |
: Opportunities, Challenges and Implications for Policy Makers |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
[París] : |
| Name of publisher, distributor, etc. |
OECD, |
| Date of publication, distribution, etc. |
2021 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
69 p. : |
| Other physical details |
gráf., mapas; |
| Dimensions |
; 1 documento PDF |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
texto (visual) |
| Source |
isbdcontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
electrónico |
| Source |
isbdmedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
recurso en línea |
| Source |
rdacarrier |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Bibliografía: p. 59-67 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Artificial Intelligence (AI) techniques are being increasingly deployed in finance, in areas such as asset management, algorithmic trading, credit underwriting or blockchain-based finance, enabled by the abundance of available data and by affordable computing capacity. Machine learning (ML) models use big data to learn and improve predictability and performance automatically through experience and data, without being programmed to do so by humans. The deployment of AI in finance is expected to increasingly drive competitive advantages for financial firms, by improving their efficiency through cost reduction and productivity enhancement, as well as by enhancing the quality of services and products offered to consumers. These competitive advantages can, in turn, benefit financial consumers by providing increased quality and personalised products, unlocking insights from data to inform investment strategies and potentially enhancing financial inclusion by allowing for the analysis of creditworthiness of clients with limited credit history (e.g. thin file SMEs). At the same time, AI applications in finance may create or intensify financial and non-financial risks, and give rise to potential financial consumer and investor protection considerations (e.g. as risks of biased, unfair or discriminatory consumer results, or data management and usage concerns). The lack of explainability of AI model processes could give rise to potential pro-cyclicality and systemic risk in the markets, and could create possible incompatibilities with existing financial supervision and internal governance frameworks, possibly challenging the technology-neutral approach to policymaking. While many of the potential risks associated with AI in finance are not unique to this innovation, the use of such techniques could amplify these vulnerabilities given the extent of complexity of the techniques employed, their dynamic adaptability and their level of autonomy. The report can help policy makers to assess the implications of these new technologies and to identify the benefits and risks related to their use. It suggests policy responses that that are intended to support AI innovation in finance while ensuring that its use is consistent with promoting financial stability, market integrity and competition, while protecting financial consumers. Emerging risks from the deployment of AI techniques need to be identified and mitigated to support and promote the use of responsible AI. Existing regulatory and supervisory requirements may need to be clarified and sometimes adjusted, as appropriate, to address some of the perceived incompatibilities of existing arrangements with AI applications. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Tecnologías habilitadoras digitales |
| 9 (RLIN) |
18 |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Inteligencia Artificial |
| 9 (RLIN) |
4348 |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Big data |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
COVID-19 |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
digitalisation |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
finance |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
IA applications |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
management |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
risks |
| 710 ## - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Organización de Cooperación y Desarrollo Económico |
| 9 (RLIN) |
2843 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf |
| Nonpublic note |
Abierto |
| Link text |
Acceso al documento |
| Electronic format type |
pdf |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Informes |