Over the span of a generation, treasury organizations in manylarge companies have migrated from a reliance on spreadsheetstoward the use of treasury management systems. These solutions provide measurableincreases in cash visibility, productivity, and risk controls. Nownew technologies like data analytics, robotic process automation(RPA), and machine learning are rapidly building on thesecapabilities, fundamentally transforming the day-to-day activitiesof leading corporate treasury functions.

So extraordinary is the digital transformation of treasury thatmany believe treasury teams of the future will primarily be engagedin value-added activities in areas like optimizing cash flows andcurrency management, instead of routine transactional tasks.Datadriven insights that staff acquire from analytics tools willalso be increasingly valuable to other parts of the enterprise.

Expectations are that within five years, most treasurymanagement systems will include embedded RPA—which automatesrepetitive processing tasks using rules-based logic. In fact, somevendors have already started to integrate these technologies.Robots automatically access, assess, and process huge data volumesinvolving cash positions, interest rates, payables, receivables,and foreign exchange rates, performing these tasks faster and moreaccurately than humans could.

Algorithms in RPA tools can also instantly highlight anyvariances from expectations, illuminating a path toward reconcilingissues. When RPA is further augmented with data analytics, treasurycan develop more transparent and precise forecasts of long-termliquidity.

Within 10 years, treasury platforms will “look and act like notreasury management system today,” says Bob Stark, vice presidentof strategy at Kyriba, a provider of a cloud-based treasurymanagement system.

Intensifying Pressures

The future may feel a bit scary for treasury professionals, astheir roles will have to change. But the consensus among manytreasury experts is that the function is headed for its heyday.Just in time, too, given the recent regulatory upheaval increasingthe compliance obligations of finance.

Several new rules—among them Basel III, Dodd-Frank, IRS Section385, and changing Public Company Accounting Oversight Board (PCAOB)audit standards— exemplify the increasing and complex pressures onmany treasury organizations. In some cases, these regulationscompel corporate treasury groups to take on additionalresponsibilities, some of which were previously assumed bybanks.

“We're at the threshold of momentous changes ahead,” Stark says.“Chief among them are the responsibilities of treasuryorganizations and the roles played by treasury staff. The currentparadigm of treasury reconciling bank accounts will shift more andmore toward strategic and analytical decision-making. Treasury mustagain rethink where it can best provide service.”

He is referring to the paradigm shift of the 1990s, whentreasury workstations first came on the scene. At the time, thesesystems were purchased almost solely by very large organizations.Gradually, treasury organizations at midsize and smaller companiesbegan shifting from spreadsheetbased cash management, reporting,and forecasting processes to using the automated platforms.

“Treasury management systems resulted in the need for treasuryto relinquish certain processing responsibilities, once moreefficient tools were available,” Stark says. “New rolesmaterialized within treasury, increasing the strategic importanceof the function. We're now at a similar juncture.”

Other treasury experts agree that the function is now at acrossroads—one posing momentous changes in the services providedby, and capabilities of, treasury staff.

“On the one hand, technologies like RPA and machine learning arere-imagining the capabilities and purpose of treasury, but on theother hand, they also necessitate the retooling of the competenciesof the people within treasury,” says Michael Kolman, co-COO, headof business development at ION Treasury, which comprises aportfolio of seven branded treasury and risk management solutionsincluding Reval, IT2, and Treasura. “Skill sets have to change, andwill change.”

Technological Upheaval

Core business functions in enterprises across every industry areconfronting the opportunities and threats posed by digitaltransformation. Once information is digitized, a computer cancapture, access, search, exchange, and integrate different dataelements. Corporate functions that have digitized a significantportion of their data will then use this information to optimizebusiness and operating workflows. The final step in a company'sdigital transformation is to reorchestrate the organization'sdigital footprint, internally and with third parties, to create acollaborative transactional digital ecosystem.

In this journey, RPA is a critical technology because it canliberate staff from performing routine and repetitive high-volume,lowcomplexity treasury tasks. When robots execute these tasksinstead, the result is more cost-effective processing.

RPA also enhances accuracy, since robots do the same things inthe same way every time. For compliance purposes, this leap forwardin precision reduces the risk of the dire consequences that wouldresult from an accounting mistake. RPA tools perform processingtasks at breakneck speeds on a continuous basis, in a workingtimetable that human beings could not match. The robots also arebetter at organizing data to create reports. These variousenhancements in productivity, accuracy, and compliance add up tosignificant treasury cost savings. Further, because automationreduces the chance of human error, RPA tools improve confidence inthe veracity of cash forecasts.

Both customers and vendors of treasury technology platformsbenefit. “We're using RPA to automate tasks that we had previouslyoffshored to save costs, and we are saving more in the process,”says ION Treasury's Kolman. “It makes no sense anymore for humanbeings to reconcile a bank statement or execute a payment over andover again, when robots can be designed to do the same things morerapidly and easily.”

Another plus is that RPA presents the opportunity for systemusers to effortlessly upgrade the product. “Every software systemneeds to be periodically replaced with a new version of the sameproduct, on a three- to five-year basis in many cases,” Kolmansays. “Robotics gives us the ability to reduce the typical numberof steps involved in upgrading the system, making the upgradingprocess much faster.”

He explained that the robots simply perform the repetitive stepspreviously performed by humans. If there were previously 20 stepsinvolved in upgrading the systems, there may be 5 steps now becausethe robot has already performed the other 15.

In addition to these benefits, RPA facilitates easiercommunications with other bestof-breed systems within treasury,such as currency management tools. Data can be captured andtransferred from one system to another with ease, permitting theintegration of different data elements via application programminginterfaces (APIs).

Different functions within a business, like financial planningand analysis (FP&A) and supply chain management, couldcertainly benefit from betterinformed cash flow and cash forecaststo support their decisionmaking. Small wonder that the buzz aroundRPA within treasury organizations is amplifying.

“In just the last three weeks, I've had multiple conversationswith clients and potential clients about RPA, wanting to know howit will affect their financial business processes, and—byextension—their roles,” says Andrew Gage, vice president ofstrategic market development at FiREapps, a provider of FX exposuremanagement software. “It is our belief that robotics, machinelearning, and predictive data analytics are altering thecompetitive landscape for treasury organizations. Data is king, andwhoever has control of it to produce enterprise value at the end ofthe day is the winner.”

Next-Stage Machine Learning

In a way, RPA is the foundation for the implementation of othertechnologies that may culminate in increasingly “smart” treasurysystems. Chief among these tools is machine learning—software thatenables computer systems to actually learn from data theyencounter. An example of machine learning is the spam filter inyour computer, which has been taught to ferret out and flagunwanted email.

“Machine learning takes your existing technology stack intreasury to the point where people become involved even later inthe process than they do presently with RPA,” says Kyriba'sStark.

For example, take the routine treasury task of capturing thecompany's cash position. Downloading details from each bankindividually is timeconsuming, and many treasury teams are alreadymoving to automate that process. What machine learning adds to theequation is the potential to quickly root out the causes of anyvariances between forecast and actuals.

“Typically, an experienced cash manager is able to dig throughthe details to unearth the specific reason or reasons” for thevariance, Stark says. Machine learning can convert that humanresearch and decision-making into an algorithm that does thechasing instead, culminating in a level of automation where answersare nearly immediate.

Thus, machine learning may accelerate cash forecasting processesand help guide more accurate and more informed decisions. “When youhave a better grasp of excess liquidity, you may decide to pay downthe credit line,” Stark says. “That's just one decision amongmany.”

ION Treasury's Kolman also extols the enhancements in cashforecasting, which he says are “top of mind” for a lot of thefirm's treasury clients. “Certainly, cash flow is an organization'slifeblood, and cash forecasting is a vital core functionality fortreasury operations,” he explains. “It's also one of their biggestchallenges. Treasury must accurately analyze cash flow, deliveraccurate cash positions, and make accurate cash forecasts, whilediscerning variances and the reasons for them. Technology providesthis heightened level of accuracy and assurance.”

Yet another benefit of machine learning is the technology'sability to quickly and efficiently identify possible instances offraud. For a treasury organization to detect fraudulent activitiestoday, a human must screen every payment. However, a rulesbasedengine could be designed to look for specific patterns that raisered flags, such as a customer opening a new bank account, billingfor certain services that are not appropriate for a particularaccount, or workflows that are atypical in some way.

Machine learning further helps a company optimize cashmanagement by identifying how much working capital the organizationneeds. “You're better able to line up different choices regardingexcess cash, such as investing it in money market funds; giving itback to the credit line; increasing longerterm debt; or doingsomething more outrageous, like working with procurement to figureout discounts with suppliers,” Stark says. “People make theultimate decision, but the analytics line up therecommendations.”

Treasury Burdens

Several surveys indicate that treasury teams can expect theircash forecasting and cash management processes to come underincreasing scrutiny, making the benefits of machine learning evenmore important. For example, in a 2016 survey by SAP, more thanthree-quarters (76 percent) of finance and treasury respondentsprojected that cash management would become more challenging fortreasury staff within the next five years, with one-third (34percent) citing a need to improve the accuracy of cash forecastingand another third (33 percent) stating that accuracy, quality, andconsistency of cash flow data are paramount considerations.

A 2018 survey by the Economist Intelligence Unit (EIU) of 300senior corporate treasury executives across multiple industrysectors echoes the same themes. According to the EIU survey, morethan 55 percent of respondents are experiencing “knock-on” effectson treasury from “sector disruption.” Respondents perceivetechnology as an effective tool for limiting this impact; 56percent cited data analytics as beneficial for the treasuryfunction.

ION Treasury's Kolman concurs. “The combination of RPA, machinelearning, and bigdata analytics offers the ability to produce moreaccurate forecasts automatically,” he says. “We've invested inmachine learning to support multiple parts of our portfolio and arebeginning to see benefits.”

It Just Gets Better

Down the line, further enhancements in these disruptivetechnologies are likely to combine in unique ways to dramaticallyalter the treasury function—and the treasury profession.“Technology will do much of the heavy lifting in treasury in thefuture,” Kolman projects.

This is a development that few would have imagined a decade ago,he adds. “After the financial crisis in 2007–08 when companiesstruggled to raise capital quickly through traditional means, therewas a heightened focus put on cash forecasting and companiesestablished 13-week, 26-week, and sometimes even longer direct cashforecasts which required significant effort and sometimes limitedaccuracy,” Kolman says. “Forecasts were built using spreadsheetswhich was the quickest way to implement.” “But we were stillbuilding out the forecasts using Excel, which remained the quickestway at the time.”

Fast-forward to today's digital transformation: “Just a decadelater, we now have the opportunity to produce extremelyhigh-quality, accurate, and near–real-time forecasts. It's a prettyastonishing development,” Kolman says.

Still, whether corporate treasuries will invest in more robustsystems that embed these disruptive technologies remains to beseen. Treasury management systems have been around for almost 30years, and their market penetration is still nowhere near 100percent. The experts we spoke with are, nonetheless, sanguine aboutprospects for future market growth.

“The future bodes well for more rapid implementation,” Kolmansays. “Treasury organizations within companies that arehistorically risk-averse when it comes to adopting technology arenow participating in treasury management system pilot programsinvolving machine learning and other automated solutions. No longeris treasury ignoring their potential. And that makes for veryexciting times.”

These “exciting times” are not happening overnight. Stark notesthat Kyriba, for example, is taking an “evolutionary—and not a 'BigBang'—approach” to building robotics and other analytical toolsinto its cloud-based treasury management system. And Kolman saysION Treasury has plans to invest incrementally in machine learning,given the technology's potential to achieve faster response times,lower total cost of ownership for clients, and ability to automatetesting. “Our mission is to enable automation through innovation,but we pursue this mission thoughtfully,” he says.

Tomorrow's Treasury Professionals

Measured approaches aside, corporate treasury will, in thefuture, be a very different organization. Expectations are thattreasury will become more of a strategic resource—not just tocolleagues in finance, but throughout the enterprise. (See thesidebar, Treasury at the Center)

Treasury at theCenter

As the treasury organization begins to reap the benefits ofsophisticated technology tools like robotic process automation(RPA), data analytics, and machine learning, it is poised to becomea centralized hub of financial insights for the rest of thebusiness. Corporate treasury functions that are able to move intothis role may improve cash forecasting and optimize liquiditymanagement across the enterprise.

In the very near future, treasury teams can expect to hearfrom staff in other finance groups, such as financial planning andanalysis (FP&A) and accounting, who are looking to learn fromtreasury's new, analytics-driven insights on cash flow andforecasts. Treasury teams will likely start to receive similarrequests from groups outside finance, as well—from business unitleaders to compliance and audit, among others.

“Treasury already is receiving emails from their businesscolleagues in other parts of the enterprise regarding cash flowsand working capital,” says Michael Kolman, coCOO, head of businessdevelopment at ION Treasury. “With treasury's role in the futureincreasingly transforming into a cash optimization analyst, relianton RPA and machine learning to make data-driven decisions, internalcustomers will reach out even more with their questions andconcerns.”

As this occurs, Kolman says, treasury staff have theopportunity to rethink their role to better serve thesestakeholders. It's an interesting concept, and some treasury groupsare already reconsidering how they can support the broaderorganization in areas like currency management. As new systemsfocus increasingly sophisticated business intelligence capabilitieson foreign exchange (FX) data, the resulting insights are relevantto—and being requested by—a growing audience within the enterprise,says Andrew Gage, vice president of strategic market development atFiREapps.

“When I started a decade ago in the burgeoning FX technologyspace, the system of choice for understanding currency risks wasstill a 1,000-page spreadsheet,” Gage notes. “Each one of the cellsin that massive document contained a calculation that was prone toerrors. Now we're at a point where modern treasuries are able tomanage currency in a highly automated fashion at amazing speed,sourcing data out of the ERP [enterprise resource planning] systemto make rapid, informed, and fact-based decisions. And we're justbeginning to tap what treasury will be able to do in the nearfuture.”

By connecting and integrating modern FX management systemswith other bestof-breed finance systems and applications, Gagesays, treasury will be positioned to routinely pass on informationabout currency exposures to other parts of the enterprise wherethis information can improve business decision-making.

“As data is mined out of the ERP and other source systems,it can be pulled into useful insights for FP&A down thehall—assuming this information is presented in an intuitive way,”says Corey Edens, FiREapps' chief solutions officer. “FP&A canmore quickly calculate the transactional impact of currency risksfor reporting purposes in the 10-K and 10-Q.”

Other areas of the business also may find this informationuseful in calculating the currency impact to their revenue and costof sales. “A case in point is the use of currency data in thesupply chain to make sourcing decisions,” Edens says.

A wide range of stakeholders are “hungering” for thesedata-driven analyses, Gage says. He's not alone in thisperspective. “By digitalizing the responsibilities of treasurymanagers, the universe of applications elsewhere in theorganization dramatically expands,” says Bob Stark, vice presidentof strategy at Kyriba. “Treasury is in a position to provide rapidinformation on cash to anyone in the business who needs theseinsights for decision-making purposes. Leveraging RPA and machinelearning, treasury can quickly isolate data to provide usefulanalyses responding to what these stakeholders need to know. Thepotential is huge.”

Gage agrees, noting the exponential impact of this advice.“As different parts of the enterprise take advantage of treasury'sanalyses, they'll begin to ask for more things beyond just theirexposure in currency risk at the corporate level,” he says.“They'll want to know the currency risk by line of business, bygeography, by customer, and so on. They'll want other data minedfor information on what a particular currency exposure is doing totheir monthover-month income statement. Once they grab theseinsights, they'll want more and more. And what's great about thesetools is that they will comply with these demands, doing more ofthe digging as needed.”

Treasury now stands at an inflection point, where staff canleverage systems that are cloud-based, have more processinghorsepower, and include evermore-advanced analytics. Gone will bethe days of treasury staff spending hours each month reconcilingbank statements. In the future, they will be better aligned withthe rest of the enterprise, providing stakeholders throughout thecompany with valuable insights—to the betterment of the bottomline.

Tomorrow's Treasury Professionals(cont'd)

“To expect a treasury analyst in the future to do nothing morethan reconcile accounts is reductive,” Stark says. “Future treasuryroles will require traditional expertise, in addition to a range ofnewer analytical skills and decisionmaking abilities.”

Soon, treasury professionals will be expected to know how toeffectively leverage RPA, data analytics technologies, and machinelearning tools. This will likely mean companies need to providetraining for their treasury staff if they want to get the most outof these technologies.

“We're getting to a point where the volume of data coming intothe treasury organization, from different internal and externalsources, is growing at a blistering pace, with millions uponmillions of rows of highly complex data,” says Corey Edens,FiREapps' chief solutions officer. “Capturing, harvesting, andintegrating this disparate data to tell an analytical story withintreasury—and across the business—requires an intuitive use ofhighly sophisticated technology. People have to be adept in usingthese tools, which will require hiring individuals with broaderskill sets, in addition to retraining current staff.”

Kolman agrees that the technologies are becoming increasinglysophisticated, pointing to ION's ongoing development of itsTreasury Anywhere tool, which is being designed for users'interactions on a mobile device. “Right now, we're experimentingwith biometrics and voice-activated software to use the tool,” hesays. “You can ask 'What's my cash position in Europe?' and thedevice will answer back. This stuff is no longer science fiction;it's real, it works, and it's coming.”

Science Wins the Day

The shift in status of the corporate treasury function from thatof a technical resource to more of a strategic business partner isconsistent with the predictions of the 2016 SAP survey on thefuture of cash management. Nearly two-thirds (62 percent) ofrespondents said that treasury must expect to take on higher-valueactivities in the future, providing deeper and faster insights oncash positions and forecasts to senior management and other partsof the enterprise. This shift is now occurring, the intervieweesconfirmed.

Meanwhile, treasury technology platforms will do more and moreof the menial, manual work that people previously had to perform.Within the not-too-distant future, treasury organizations willshrink, as both automation and analytics technologies absorb muchof the current treasury workload. Some people will be displaced.Others will need to add value through their skilled use oftechnology to analyze and interpret the results of the dataanalyses.

The rest of the enterprise will call upon these highly trainedanalysts to provide advice on their own needs for strategic cashmanagement. In the SAP survey, 82 percent of respondents predictedthat non-treasury staff would reach out to treasury in the futureto better understand their cash positions. “One can easily imaginetreasury having a seat at the strategy table to assist complicatedconcerns and deliberations over more complex risks andregulations,” says FiREapps' Edens.

Ten years from now, spreadsheets will likely have gone the wayof the abacus; young treasury professionals will read about Excelin their college history-of-finance classes. “Treasury departmentsshould create their future vision today where repetitive tasks areautomated and liquidity forecast accuracy improves, because withnew technology the vision will be achievable,” Kolman says.

Stark concurs: “Now is the time for treasury to re-imagine wherethey can provide the most value tomorrow.”

Also from the November 2018 Special Report:

SPONSORED STATEMENT –FiREapps: CombiningBI Tools & Enterprise Currency Analytics

SPONSORED STATEMENT – ION: Sevencrucial tips for selecting the best Treasury Management System foryour business

SPONSORED STATEMENT – Kyriba: How Technology Creates Intelligence in Treasury