Data-Driven Treasury

In a period of uncertainty, when cash is king, treasurers are looking for greater access to data to optimize liquidity management.

As a retailer, Canadian Tire operates a cyclical business, with inventory tied to the seasons. Priya Kurian, assistant treasurer for Canadian Tire, says the Covid-19 pandemic shifted her focus to real-time rather than historical data.

Luckily, Canadian Tire had installed all six modules of GTreasury’s cloud-based treasury management system toward the end of last year, providing real-time access to increased amounts of data.

While her team certainly had access to data they studied to make educated adjustments to estimates before, “now we’re getting more forward-looking data,” she says, “and we have access to people that can help us run modeling to get real-time estimates for areas that are more sensitive and have big impacts to cash flows.” It has led to a shift from quarterly cash reports for the C-suite to daily dashboards “with real-time updates for the next three weeks of cash and fairly constant revisions looking out for the next year.”

Real-time data has been useful in the positioning process, and it has also freed the treasury team from some data work. “As we built in automated integration for many feeds, there’s a lot less time being spent by my team just inputting data and adjusting things manually,” Kurian says.

Better and more-timely identification, measurement and understanding of key liquidity drivers are crucial for corporations attempting to manage and mitigate liquidity risks during the pandemic. For this, consolidation and data quality is extremely important.

“There’s no point in trying to make decisions from your data until you trust the quality,” says Sonny Singh, senior vice president and general manager of Oracle’s Financial Services Global Business Unit. “But by nature, timeliness and data quality are often at odds. Traditional ways of reconciliation and data quality control can create severe bottlenecks.”

Organizations need to make sure data is checked for quality and reconciled with finance and then made available to business users as soon as possible, says Singh:. “This is where a flexible cloud infrastructure with the ability to easily integrate new technologies is essential in bridging the data divide. We can now manage traditional requirements and provide information at a near-real-time pace using APIs.”

More-powerful cloud infrastructure also helps treasury to make better use of the data the company holds, to better serve its clients and third-party providers.

“For decades, corporate and bank treasurers have been sitting on large amounts of data without making use of it,” says Pedro Porfirio, global head of Capital Markets at Finastra. “Traditional systems don’t typically have the ability to link disparate data sources nor rationalize data into meaningful data sets. As a result, it is difficult to obtain a holistic view of the customer; and businesses are unable to personalize the customer experience and offerings.”

Three Paths for Institutions

Kurian hopes real-time payment and data-exchange initiatives will encourage banks to offer data analysis services to their clients. “Banks should be able to do more with the wealth of data they have, to help us better understand the data,” she says.

That’s where choices come in.

“Larger institutions can try to solve this by building their own data infrastructure–enabling technology, resources, data architects, engineers and so on,” suggests Porfirio. “But this comes at a cost and is often not an option for smaller institutions. A second option is for institutions to embrace collaboration by partnering with fintechs and bringing outside innovation in.”

“Another route to explore is third-party partnerships with companies offering cloud-based platforms, such as—where data can be made available securely—that provide access to an ecosystem of analytical solutions, fintechs and other third parties,” says Porfirio. “This creates an opportunity for corporate treasurers involved in the ecosystem to capitalize on Big Data, enabling them to make smarter decisions.”

The pandemic has helped identify business levers for treasuries considering similar data integrations, says Kurian.

“The more you talk to the business, the closer you get to the drivers [of decision-making] and what that means to cash flow,” she says. “Where I feel we’ve been successful is that we took the very large swings we saw in the pandemic and used the opportunity to uncover those drivers. Through this work we’ve been able to make a lot of refinements.”

Andrew Hollins, director of Foreign Exchange and Corporate Treasury at Refinitiv, says data analytics can help with cash management and cash flow forecasting—funding the business, investing surplus cash, market monitoring, executing transactions and engaging in risk management and hedging.

Without a dedicated credit risk department, corporates are unsurprisingly heavily reliant on credit market data to analyze credit risk. “The source of this credit data could be the CDS [credit default swap] market, the corporate bond market, income statements and balance sheets or rating agency data, and [it can also be] generated from applying spreads to a risk-free rate such as Libor,” Hollins explains.

In most cases the data will be used to derive some kind of default probability score. In addition to market-observed data, there is also research-derived credit risk data such as the credit risk models from quantitative analytics platforms, including Refinitiv’s StarMine as well as the CRIS Index data developed by Singapore University for nonlisted entities. “This specialist, research-derived data can have a critical role to play, since in certain cases market-observed data may overlook factors that can be instrumental in impacting default probabilities,” says Hollins, insisting that both StarMine and CRIS Index data can be used by corporates as part of their toolbox to analyze credit risk.

Data Sharing and AI

Treasurers are seeing the value of efficiently storing and collecting all their data, as well as the ability to share with finance and their various lines of business, says Singh.

“This helps in setting competitive pricing, identifying where to improve margins and creating workflow efficiencies,” he says. “Add artificial intelligence and machine learning models on top of that data, and the insights gleaned and possibilities for operational improvement are endless.”

A data lake can be even more powerful when corporate treasurers and banks use it in conjunction with a platform economy by sharing access to their data, says Porfirio.

“This is something treasurers need to get used to, and it is a two-way street,” he argues. “Corporate treasurers agree on sharing anonymized data, and they can then access anonymized data from other companies. This improves the database; which banks can then access to inform their decisions. The result is that banks can use data intelligence to provide better services and give optimized advice to corporate treasurers. Microsoft Azure Data Share is a good example of technology enabling this,” says Porfirio

However, in today’s interconnected world, real-time payments are increasingly the norm. Andy Schmidt, vice president and global industry lead for Banking at technology and business consulting firm CGI, emphasizes that proper risk management and liquidity management are therefore more important than ever.

“Having a free flow of information between payers, payees and their banks is crucial, especially now,” he says, “given that corporates are seeking greater visibility into their cash flows so they can make the best possible decisions taking in their cash position, their relationship with their bank and their desire to keep their business and trading partners happy, at the same time.”