AFTAs 2019: Best New Technology Introduced Over the Last 12 Months—AI, Machine Learning and Analytics—ActiveViam


Following the global financial crisis, the banking industry has had to deal with more stringent risk capital requirements that demand agility, flexibility, speed, and ease of communication across traditionally siloed departments. Banks also needed a firm grasp of their enterprise-wide data to meet regulatory requirements, and also to ensure a return on capital. It is for this reason that Allen Whipple, co-founder and managing director at ActiveViam, says it makes sense for any regulatory solution to pivot from prescriptive to predictive analytics. 

ActiveViam topped this category at this year’s AFTAs due to its FRTB Accelerator, part of a suite of Accelerator products that it launched in the past year. The products contain all the source code and formulae to meet a particular set of regulations and/or business requirements. In this case, it was those needed for the standardized approach (SA) and the internal model approach (IMA) risk framework, which stems from the Basel Committee on Banking Supervision’s Fundamental Review of the Trading Book (FRTB). 

The FRTB Accelerator includes capabilities such as the capital decomposition tool, which provides clients with the ability to deploy capital across an organization more precisely. “This allows a client to take risk management a step further and perform predictive analysis, which can be applied to broader internal market risk scenarios,” Whipple explains. He adds that banks can perform limit-monitoring and back-testing, which allows them to stay within the scope of their IMA status. 

Looking ahead, ActiveViam will add a product for Python notebooks to facilitate data science work, reducing the time it takes to move from data to insight. Quants will no longer need to switch between notebooks, data visualization tools, and end-user business intelligence applications. Using the ActiveViam Python Library, they will be able to create dashboards and share them within the same environment. “Coders can do everything in Jupyter—or a Python notebook of choice—from beginning to end,” Whipple says. 

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