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Steps on a Technology Journey to Data-Driven Decisions, Actions

By February 11, 2020No Comments

A detailed look at the progression of one tech company’s digital transformation.

Building and using a data lake that is a centralized source of data is a key part of the modernization and technology journey for treasury at one leading tech company; a progression from being data aware to data proficient to data savvy to achieving data-driven decisions and actions.

  • A treasury cash operations manager presenting details of this journey at a NeuGroup meeting said a top priority was “wading into the data lake despite the complexity” because treasury is “tired of pulling data from multiple systems.” Among the goals are data transparency, standardization and control.

Data analytics. This company’s treasury adopted Power BI (everyone is trained to use it) for creating standardized dashboards with drill-down capabilities so it could address questions immediately but also create a more self-serve environment. Using the tool’s capabilities to do data analytics, the member said, reveals both data-driven answers and, initially, data shortcomings.

  • Hence the benefit of the data lake. And as other NeuGroup members have noted, treasury’s ability to use AI and machine learning for cash flow forecasting and other purposes depends on having data that has depth and detail.  

The role of the cloud. The presenter said that moving treasury applications to the cloud to co-locate data reduced IT’s footprint by 60%. Treasury has about 40 applications; 24 of them are first-party apps (meaning the company builds and maintains them in-house), and the rest are third-party apps. The first-party apps address:

  • Cash forecasting
  • Cash visibility
  • Bank account management
  • Intercompany loan management
  • Wire requests and tracking

SWIFT gpi and transparency. The company was an early adopter of SWIFT gpi, which allows treasury to track wires once they leave treasury’s banking partner. One member said this is good news for her treasury. “This will be very beneficial to us as we have limited visibility to transactions once they leave our banks,” she said. “SWIFT gpi will provide more transparency on payment statuses.”

SWIFT and the cloud. The company and SWIFT have worked together on a cloud-native project that allows SWIFT wire transfers to be done over the cloud. The teams have enabled SWIFT wire transfers on this setup. The company is the first cloud provider working with SWIFT to build public cloud connectivity and will work toward making this solution available to the industry. 

How it works. Treasury sends a wire instruction through a web app on the cloud, which is validated by using machine-learning algorithms.

  • Once validated for authenticity, these wires are sent to SWIFT via the company’s SWIFT installation on the cloud. SWIFT validates the wire instructions and sends it off to the appropriate bank. Once the bank carries out the wire instruction, it sends confirmation through to treasury.

Machine learning. Treasury built a machine-learning forecasting solution that is addressing a key FX exposure for the company while improving forecast accuracy of AR and operational efficiency for the team.

  • Historical data in the cloud was cleaned and used to create the solution using the R programming language and other tools.
  • Cumulative forecasting of notional exposure improved by 6%.
  • Volatility of FX impact on other income and expense was reduced by ~25%.

The people part. Like many treasury teams, this one is trying to strike the right balance between people in possession of core treasury knowledge and skills and those who are more adept at data analytics and have advanced quantitative abilities. In addition to everyone learning Power BI, treasury recently hired its own data analysts.

The problem remains, though, that some staff lack the skills, ability or interest to learn new tools and technology at a point when data science is increasingly critical. The treasury team highly encourages employees to take courses to increase their skills in the data analytics space and provides sponsorship to help them achieve these goals.

Antony Michels

Author Antony Michels

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