AFTAs 2019: Best Middle-Office Initiative—AxiomSL

AxiomSL-MiddleOffice-AFTAs2019

AxiomSL wins the best middle-office initiative category at this year’s AFTAs, thanks to its ControllerView data integrity and control platform. In recent years, the need to show improved levels of granularity and transparency across almost every business process has become one of the most pervasive technology drivers across the capital markets. “Black-box-generated” numbers are unacceptable to risk and compliance officers internally and regulators and end-investors externally. Now, transparency and auditability are expected by all constituents. To that end, ControllerView is designed to deliver transparency across a number of processes, including data sourcing, aggregation, enrichment, pre-processing, validation and reporting. 

According to Harry Chopra, AxiomSL’s chief client officer, ControllerView allows users to drill down into the methodologies underpinning the calculations so that they can be confident that the numbers generated are reliable and accurate. This is especially pertinent to their tier-1 capital ratios—the ratio of core equity capital to risk-weighted assets—calculations that are critical not only for regulatory purposes but for capital optimization, too. “We add lineage to the transparency in the calculations so that users know exactly where the numbers are coming from,” Chopra explains. “We also help firms optimize their collateral—if they’re going to take a large haircut, we have algorithms that help them understand how they can improve their tier-1 capital ratios.”  

As for what users can expect from AxiomSL over the next 12 months, there are two themes: improving ControllerView’s speed and ease of use, and incorporating machine learning technology to enhance data quality. “We’re currently working on formalizing our Apache Spark architecture,” Chopra says. “We’ve also just identified an appropriate machine learning use-case. The way the system works is that when data is ingested, we have a pretty good idea of what it should look like. We then use machine learning technology to identify outliers in the data, which means we can significantly improve data quality as it flows into the system.”  

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