[交易策略] New frontiers in deep learning and quantitative finance: an overview [推广有奖]

Artificial Intelligence, MachineLearning and Big Data in Finance Opportunities, Challenges and Implicationsfor Policy Makers



Artificial Intelligence (AI) techniques are being increasingly deployed in finance, in areas such as assetmanagement, algorithmic trading, credit underwriting or blockchain-based finance, enabled by theabundance of available data and by affordable computing capacity. Machine learning (ML) models use bigdata 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 enhancingthe quality of services and products offered to consumers. In turn, these competitive advantages canbenefit financial consumers by providing increased quality and personalised products, unlocking insightsfrom data to inform investment strategies and potentially enhancing financial inclusion by allowing for theanalysis 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, andgive 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 ofexplainability of AI model processes could give rise to potential pro-cyclicality and systemic risk in themarkets, and could create possible incompatibilities with existing financial supervision and internalgovernance frameworks, possibly challenging the technology-neutral approach to policymaking. Whilemany of the potential risks associated with AI in finance are not unique to this innovation, the use of suchtechniques 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 thebenefits and risks related to their use. It suggests policy responses that that are intended to support AIinnovation in finance while ensuring that its use is consistent with promoting financial stability, marketintegrity and competition, while protecting financial consumers. Emerging risks from the deployment of AItechniques need to be identified and mitigated to support and promote the use of responsible AI. Existingregulatory 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.