计算XVA的复杂性加上满足法规和平衡风险资本的需要对所有银行来说是一项挑战。出于这个原因，Danske Bank创建并开发了一种创新的解决方案和方法来解决它。

丹麦’s largest bank, Danske Bank, was ahead of the curve in managing the complicated task of xVA, creating an advanced system for calculating xVA figures known as Adjoint Algorithmic Differentiation (AAD).

However, they needed an equally advanced system to match the AAD output, one that would enable real-time risk analytics. Common solutions were unable to cope with the massive volumes of data or offer an interactive experience with on-the-fly drill-down capabilities and multidimensional analysis.

由于其经过验证的表现，该银行转向他们在内部使用的东西进行市场风险。他们选择自2015年以来已经在银行其他地区使用的ActiveViam的居民，并使他们能够满足XVA挑战并在记录时间内实施解决方案。

ActivePivot是ActiveViam开发和发布的内存数据分析和聚合引擎。交付给Danske的XVA桌面的ActivePivot实施是能够对整个衍生品组合进行实时分析，并管理XVA桌面的许多活动部件。

该部署允许Danske Bank分析师实时钻取到最粒度细节水平，检查异常的原因，并根据需要进行调整，无需不断进入它来申请帮助。在许多银行中，IT资源有时可以稀缺，并且对业务团队提供的自治活动是选择ActivePivot的另一个关键原因。

正如尼克拉斯塞伦那个Danske银行XVA桌子的负责人，“The ability to look at all the different risks was a huge step for us, saving us a lot of time not just in a single day but for every single end of month - for the full year.这是我们的真正好处 - 如果有些东西看起来很奇怪，可以在几秒钟内快速钻取，而不是数小时或几天。.”

Overcoming the big data challenge

xVA酒店代表resents a huge challenge for banks, especially under current circumstances as the 2020 global pandemic has shaken the global economy and caused a large number of credit impairments and loan defaults, which in turn, can affect any number of elements in a derivatives contract.

Mapping costs to a specific counterparty’s netting sets is a crucial task in this scenario and one that the xVA desk is responsible for.

Managing xVA requires the analysis of an enormous amount of data, and therefore a set of highly sophisticated tools. This is essential for business performance as mismanagement of the xVA numbers can negatively impact a bank’s ability to generate revenue.

“ActiveViam technology came into play when we realized that the number of market data elements and risk sensitivities and the level of granularity we needed represented a volume of data so huge that we couldn’t deal with it in a timely fashion to explain changes in risk and changes in value,” Mr. Rasmussen said.

The solution, built by Danske Bank’s teams with support from ActiveViam, readily surmounted the massive task of aggregating xVA data, making it digestible for risk analysts to manage successfully. Since the implementation, the use of ActivePivot has grown to keep pace with Danske Bank’s hierarchies and instrument classifications as they have evolved and expanded over time – without the need for any analytical modifications since the data and the analytics remain separate.

查看案例研究“Danske Bank：克服XVA挑战”to learn more about this achievement.