000			03898nam a22003497a 4500
001			00006677
003			ES-MaONT
005			20211006062654.0
008			210825s2021 fr db||f t|||i00| 0 eng d
040			_aES-MaONT
245	0	0	_aArtificial Intelligence, Machine Learning and Big Data in Finance _b: Opportunities, Challenges and Implications for Policy Makers
260			_a[París] : _bOECD, _c2021
300			_a69 p. : _bgráf., mapas; _c; 1 documento PDF
336			_atexto (visual) _2isbdcontent
337			_aelectrónico _2isbdmedia
338			_arecurso en línea _2rdacarrier
504			_aBibliografía: p. 59-67
520			_aArtificial 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	_aTecnologías habilitadoras digitales _918
650		4	_aInteligencia Artificial _94348
653			_aBig data
653			_aCOVID-19
653			_adigitalisation
653			_afinance
653			_aIA applications
653			_amanagement
653			_arisks
710			_aOrganización de Cooperación y Desarrollo Económico _92843
856	4	2	_uhttps://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf _x0 _yAcceso al documento _qpdf
942			_2udc _cINF
999			_c6677 _d6677