BOE调查显示了对外包银行服务的积极Covid影响

总体而言，半数受访银行表示，由于大流行的影响，机器学习和数据科学的重要性有所增加，没有一家银行认为COVID-19危机会降低机器学习和数据科学对它们的重要性。近40%的受访者表示，机器学习和数据科学对未来业务的重要性有所增加，另有10%的银行表示，重要性大幅增加。没有一家银行报告称机器学习和数据科学的重要性有所下降。约三分之一的银行表示，正在进行的机器学习和数据科学应用程序数量有所增加。然而，只有16%的银行报告现有申请的资金和/或资源有所增加，类似的银行报告有所减少。同样，近35%的银行报告计划申请的数量增加，但只有23%的银行报告计划申请的资金和/或资源增加，11.5%的银行报告计划申请的资金和/或资源减少。与此同时，约35%的银行报告了对机器学习模型性能的负面影响。这可能是因为大流行造成了宏观经济变量的重大变化，如失业率上升和抵押贷款忍耐，这需要重新校准机器学习(以及传统的)模型。大流行创造了一个重​​大的低迷，这是无法在经济数据的基础上或历史预测因素的基础上预测。

此外，小型英国银行之间存在显着差异，涉及其使用第三方供应商产品和服务。较小的银行报告了“积极的”影响（例如，在Covid-19关于所有类别的数据科学和机器学习方面的绩效，影响，使用），数据收集和模型测试和验证是最大的区域"positive’ impact. Large UK banks reported a "positive" impact on use of outsourced platforms and infrastructure. These findings are in line with market intelligence that smaller banks are looking to increase their use of off-the-shelf machine learning products. This stands to reason, given the generally more substantial in-house data and analytical capabilities of large banks. Alongside the usual risks associated with outsourcing, the use of machine learning and data science can pose additional risks and challenges. For example, outsourced machine learning models may be more difficult to interpret because detailed knowledge in terms of how they were developed resides outside the bank. This can make it more difficult for banks to understand how the model works and to monitor performance, which could result in unexpected or unexplained performance and risks materializing. If multiple banks use the same third-party provider and machine learning model, this could also potentially lead to an increase in herding, concentration, and even the possibility of systemic risks where methodologies are common.

相关链接

• 通知
• 详细的调查结果

关键词:欧洲、英国、银行业、新冠肺炎、机器学习、人工智能、Regtech、大数据、外包安排、第三方安排、京东方