Schiphol-Rijk – Full-time Read more
Amsterdam – Full-time Read more
An active liquidity network allows companies to avoid multiple costs and delays by globally managing liquidity across their subsidiaries. With 500 banks involved and over 40,000 payment formats to use, this is already a reality for over 2,000 Kyriba clients.
I am often asked, what is an “Active Liquidity Network”? Actually it’s the very foundation of the Kyriba platform, but let me use a simple example to illustrate what it is and the difference it makes.
Technology is providing us with so many great options for everyday life activities. Take the humble takeaway. Not so long ago you’d call up, your order would be placed in a manual ordering system, food would be prepared and then it would be delivered. Today the takeaway experience can be very different. You will order on a mobile device or with a delivery service or by voice or Messenger. The delivery service tells the kitchen what food to prepare, conducts all the billing and organises the food to be couriered to you. While the cooking of the food is still manual, everything else is managed by cloud-based technologies, and you have lots of options, each with their own take on how to make your takeaway experience better, faster, cheaper.
The same thing is happening within businesses. SaaS technology enables your corporate teams to work more autonomously with a resource-planning package that is more bespoke to their task. The original ERP is being unbundled and focused on aggregating accounting entries from various other systems. These bring great benefits to your company’s ability to compete in the marketplace, making you better, faster and cheaper. But given that many of these tools are able to instruct or make payments, this introduces a hazardous landscape for currently accepted liquidity management and control practices.
The problem is further exaggerated by the global expansion that has taken place in the last 20 – 30 years. Technology isn’t just providing more options for how a corporate plans its resources. It’s also providing better, cheaper, faster options for how payments are made and received. Each approach has its own pros and cons. The upshot is that there are many more providers today conducting more payments in more innovative ways, but this innovation, while opening up new choices, also makes the payments landscape more complex.
All this hasn’t stopped an explosion in electronic payment volumes. This is an unstoppable trend that demands a more robust way of controlling and managing payments in and out of business of any size, just as a restaurant receiving 1,000 takeaway orders a night will need to move away from servicing orders on pen and paper. The risks, the costs, and the lack of speed and optimisation are all too great.
The challenge you face
Now, let’s look at a corporate example to illustrate the challenge. Let’s assume a multinational group has a subsidiary in Birmingham, in the UK, which needs to make payments for goods and services to suppliers in Romania and Turkey. The subsidiary has its operating bank account with TSB and is using the bank’s SMB portal to manage cash and make payments. Its ERP system is connected with the bank’s portal for automatic payment file upload. At the same time, the company has subsidiaries in Romania and Turkey that also have a similar setup with their local banks. It all looks good and well-automated everywhere.
But to actually make a payment to a Turkish or Romanian supplier, the Birmingham-based subsidiary’s treasurer has to go through the following steps: approve a foreign currency payment; agree to the exchange rate offered by the bank, which is given without reference to a spread of interbank rates; wait for one or two days for the other FX rate to settle; wait one or two days more for the payment to be cleared by TSB via Swift and the corresponding bank network; wait some more until the supplier confirms they have received the funds and made a shipment; and finally reconcile it all manually with the ERP system.
As a result, the subsidiary incurs the FX spread, swap rates on every payment up to 100 basis points, and interbank transfer fees for every payment of £20. There are also three further delays before the funds reach the beneficiary accounts and manual reconciliation of the ERP. And that happens with every payment for every subsidiary every day!
It’s a pity that the Birmingham-based company doesn’t know that group company subsidiaries in Romania and Turkey have plenty of lei and lire in their local bank accounts. Or that they are connected to their domestic clearing systems providing same day or in real-time clearing and automating confirmation, or no fee at all. Or that there was a better, faster, cheaper payment option the corporate could easily connect to.
How an Active Liquidity Network works?
Let’s look at a different way of doing this. Imagine that the group chooses Kyriba and gets on board the Kyriba global SaaS platform. All of its subsidiaries – including those in the UK, Romania and Turkey as well as headquarters – and all of those subsidiaries’ ERP systems – are then connected to Kyriba for payment, invoicing, and cash flow upload as well as for GL entry reconciliation. Over 2,000 customers and 65,000 legal entities are live today. Kyriba offers automated bank connectivity via secure SFTP and now bank API with more than 500 banks worldwide and growing. And our bank format libraries have more than 40,000 formats and variances supporting payment originations from more than 100 countries in payment delivery to more than 130 countries. Using Kyriba, the payments submitted by the UK subsidiary will be automatically converted to the relevant domestic clearing formats and submitted to those banks the same day.
What difference does that make? With the Kyriba platform the group can internalise and optimise its payment flows. It can see cash balances and cash forecasts across all currencies and bank accounts in real time. A treasury team using Kyriba Cash Forecasting and Kyriba In-house Banking Module can net the outflows by currency and use the market to square off or net the currency positions. As soon as the payments are acknowledged by the banks in real-time or (worst case) next morning, the confirmations and automated dual entries can be imported into the UK subsidiary’s ERP for automated reconciliation.
Better still, the company can use offers like Kyriba Pay, powered by partners like NatWest, that offer competitive and transparent FX spreads with no hidden fees attached. They can choose to use the liquidity they have in lei, lire or other currencies to make the payments without FX conversions at all. That means no interbank fees, globally optimising the effects of exposures and costs, and making same-day payments to 130 countries with automatic dual reconciliation.
That’s what we mean by an Active Liquidity Network. Ours is already the largest in the world, and growing by about 30% annually. It is the foundation of the Kyriba platform that enables our Treasury payment factory risk management and supply chain finance applications, as well as many other value-added services. We are already processing 17 million transactions on behalf of our customers on an average day. We will continue to innovate our existing propositions.
The world’s connectivity is moving to open API. We are pursuing that in three ways.
First, Bank API Connectivity: we have completed pilots with two global banks already, and will be delivering many more in 2021. Secondly, ERP API Connectivity, leading to ERP connect on marketplace, and thirdly Kyriba Open API, to turn the Kyriba active liquidity network into an open API platform for customers, partners and fintechs. This is what we call the Kyriba Active Liquidity Network.
It is here right now and you have a choice to make. Deal on your own with the growing size and complexity of managing liquidity at global scale on time, with speed, accuracy and efficiency . . . or join the 2,000 corporations who are doing it by leveraging the Kyriba platform, and really drive the value of your business.
Şişecam is a Turkey-based, multi-national glass manufacturer that wanted to centralise payments, get better visibility of the group’s accounts and reduce the potential for fraud. Kyriba helped them achieved all this – and more.
Barış Gokalp, Head of Treasury at Şişecam explains the background to the project: “when I joined Şişecam, it was very decentralised, with each company managing its own banking operation. We had too many banks, over 60 companies and multiple ERP systems. After 2013 we did a lot of M&A so there were various different ERPs. There was also a lots of connection types, including SFTP, fax and email, with no standardisation. Each payment operation had its own route, which made it hard to manage.”
“We realised that first we had to solve the connectivity issue with the banks. We figured out that we were spending a lot of time answering how much money do we have and also on the banking operations for our payments.”
Levent Coskuner, Managing Partner of ELC Strategy which advised Şişecam, explains the approach taken: “we knew the internal culture and structure of financing at Şişecam, so we were looking for the best global solution. Between his arrival at Şişecam and the end of 2018, Barış and I visited various countries to understand the different options. It was very important that the solution was very scalable and secure – security was one of the main issues. And given that they have multiple ERPs, we needed a standardised approach. Kyriba has the number one SaaS solution.”
The project had several key elements. “The focus was on enabling payments for ERP systems, centralising and securing them,” says Nik Romano, Head of Emerging Markets at Kyriba. “But they also wanted to gain visibility into the group’s bank accounts. Şişecam selected us as much on the capability of our technology from an application perspective as on the capability to enable connections across so many banks and so many jurisdictions.”
When the Şişecam team looked at Kyriba’s references they realised that a lot of companies have worries about transactions, and that was one of the key points in their decision.
“The number of transactions is not important to us, rather the variety of those transactions. We saw that our geographic reach – Kyriba’s and Şişecam’s – matched, and when we visited Kyriba clients to get references the feedback was marvellous!” says Gokalp.
Tackling supply chain finance was not on the initial agenda, but when the Şişecam team visited a Kyriba client in France they realised that they could also use the treasury management system for other parts of their treasury activities. So although they began with account visibility and payment operations, they realised that they could also include supply chain finance, FX management, cash flow management and cash flow forecasting.
“As the treasury director I saw that we could manage all our treasury activities on one platform with many banks, many countries and many companies. Perfect!” says Gokalp.
“We began to go live with the various countries within the Şişecam group, and by the end of 2021 we will have finished that. All the connections will be established and all the payments will be done via Kyriba. We have also begun to sort out the supply chain finance issues and we will plug the banks into our supply chain finance because we know that a company’s strength comes from its suppliers. In addition, we know that we can manage our FX position via Kyriba. So we will look at that and, if we can manage to finalise things, we will also use Kyriba’s cash flow management module by the end of next year,” says Gokalp.
Gokalp agrees that fraud was the key motivation for the group’s top management. “As all treasurers know, we need to do the checks before the money leaves,” he says. “You should establish in your workflow rules, so that if there is some ‘noise’ around a payment, you can stop it. We have begun to follow where the money is going and when it will reach us. I hope that by the end of the next year we will be fully digitalised, which is one of the objectives of our organisation. The payment file will come from the ERP and no one will be able to touch it, it goes directly via Kyriba.”
Full digitisation means that when a file is created it goes directly and securely to Kyriba, through the approval process and on to the bank. The ERP and the accountants can see in a couple of minutes what has happened to the payment and, if there is a rejection or some other problem that is also reflected back to the ERP system. This is a fully integrated process.
As with so many clients, the Covid crisis showed Şişecam just what their new system could do.
Gokalp explains: “When the pandemic hit we were initially using Kyriba with five companies in Turkey, but in two days all the companies were able to use Kyriba for payments. So the need for the people to come into the office for the signatures and approvals – that was all removed. That was a big credibility boost for the project as well. Before, it was very hard to make a payment. You sent it to the bank and then it arrived, or, if it didn’t you just sent it again. But now all this is done in 10 minutes max.”
“At first some people internally were worried about this project, but when they understood what the project entailed, they too wanted to be part of it.”
Şişecam is one of the biggest glass manufacturers in the world, based in Turkey but with operations in the Eurozone, Russia, India and Egypt. The group manufactures all sorts of glass – table glass, glass packaging, flat glass and automotive glass – and also produces the chemicals used to produce glass. It has 20 companies worldwide and is working with approximately 60 banks.
Someone recently asked Joe Marcin, “What does Kyriba really do?” he thought about it for a moment and although Kyriba solves some really complex problems for their customers, it really comes down to a pretty simple answer.
At Kyriba, they help some of the world’s most well-known companies, government entities, and financial institutions answer these 5 essential questions:
- How much money do I have?
- Where is it?
- How much money will I have in the future?
- How do I optimize the way I move my money across financial institutions, legal entities, and international borders to lower risk and minimize costs?
- How do I turn my money into a growth asset by investing it in ways that yield higher returns while not increasing risk or lowing my access to liquidity whenever I need it?
Enterprise Liquidity Management is transforming the office of the CFO from a cost center to a profit center for customers all over the world. That is why the Kyriba customers trust them to manage TRILLIONS of dollars for them every day.
See some of the success stories here: https://lnkd.in/gp7sZMW
Contact Kyriba directly for more information.
For the past 10 years we have lived with an overabundance of liquidity. In most people’s minds, abundant liquidity means constant availability. But the subprime crisis, the European debt crisis and now the COVID pandemic have shown the opposite to be true.
In a world of extreme volatility, liquidity flows can be interrupted overnight. And for financial managers therein lies the paradox. Despite its overabundance, it has never been more crucial to secure, diversify, monitor and optimise liquidity.
Prepare for the unthinkable.
In this environment, liquidity is obviously strategic, but above all it must be seen as a volatile and fragile resource, especially vulnerable to market disruptions whose occurrence and scope are unforeseeable by definition as well as by their very nature. The health crisis showed us that nothing is safe from a complete, abrupt halt, not even cash flow from operations, across every sector.
CFOs must now prepare their companies for the unthinkable! They will need to spend more and more time and energy to activate every possible source of liquidity by monitoring prices, availability, term, currencies and security packages for each of these sources. They will do this with a constant focus on optimisation, and above all must be ready to make snap decisions about sources that have run dry. It’s a massive undertaking. In a world of extreme volatility, Active Liquidity Management will make tomorrow’s leaders stand out from the crowd.
Contact Kyriba directly for more information.
MISSED PART I? YOU CAN READ IT HERE.
Not too long ago, AI seemed a distant dream for creatives in Hollywood and sci-fi movies such as Blade Runner. Today it is all around us. We carry it in our pockets, it harnesses the technology in self-driving cars and it teaches itself how to solve a Rubik’s Cube in under a second. As this technology matures, every company must ask itself the central question: Will artificial intelligence disrupt my industry? And how can it benefit me? While the world of finance may have a conservative ring to it, it is rapidly modifying to a digital future. We can already see artificial intelligence being used in many applications.
Just as Blade Runner was an artistic catalyst for future-noir narratives, so is machine learning essential for the narrative of treasury automation.
For example, machine learning algorithms are incredibly good at recognizing corrupt financial activities or identifying bank fraud since it can handle information thousand times faster than we can blink. Algorithms analyze user actions and distinguish between various types of transactions by gathering a huge amount of data (Big Data). By pointing out odd behavior, it learns over time and becomes even better at it. Another way Big Data is being used, is through credit scoring. By deciding who is eligible for a credit card and who isn’t the algorithm takes over the role of a human analyst.
Not only for analysis purposes but also for saving costs you will find different applications. Today we see customer care or cold calling getting replaced by talking bots, almost indistinguishable from human interaction, helping enterprises save a lot of time and money.
Finance is a fundamental aspect of everyday life for everyday people, all around the world. The endless potential is mesmerizing and what we now see is only the tip of the iceberg. However, the sudden shift is already delivering tangible business benefits. So where does this sudden shift come from? First off, as we’ve seen in part 1, the shift has been going on for well over a decade, silently emerging. The market is becoming more and more electronic, due to the explosion of the amount and speed of data in- and output. Secondly, the cost of running high powered computing networks came down drastically. These two key factors resulted in a trend that has never been seen before.
Crystal Ball or Digital Snake Oil?
So what exactly is true of the hype? I’ll be the first one to admit that the proclaimed revolution is often widely exaggerated and ungrounded. When it comes to stock market predictions, a monkey with a dartboard still has the upper hand compared to powerful AI tools in many occasions. Success in human-imitative AI has in fact been limited due to the complexity of human intelligence. It has layers of nuance still to be grasped. In addition, to answer on a human intellectual level is not the same as understanding the meaning behind it. Thus, the challenge of creating humanlike intelligence in machines remains greatly underestimated.
So where is the line between the crystal ball that knows all and digital snake oil? So far, the limiting factor lies in automated and repetitive processes. The basic approach only works in a closed domain with strict rules, such as chess or Go. Now if you add in the word ‘tedious’ to ‘automated’ and ‘repetitive’, you will have the perfect recipe for what is a monotonous task in finance: Managing & updating spreadsheets.
Plumbing the depths of the seemingly infinite sea of spreadsheets is still a known task in treasury, although the negative consequences are common knowledge among business departments. According to our survey, still more than 90% of companies use Excel spreadsheets for their day-to-day operations. Yet, technology doesn’t suffer from some of the dilemmas humans may face in finance which could affect people’s ability to make good decisions: Computers don’t need vacations or sleep, they are less biased and they can do the job more precise. These are obstacles in which AI, in comparison to managing spreadsheets manually, can excel.
Another factor that heavily influences the conversion to artificial solutions is the huge amount of data spread over different branches in a company. Though every department has its own responsibilities, it still has useful data. This untapped information, called Dark Data, has the potential to create a bridge between treasury and other branches, leaving more room for actual analysis between departments within. It goes without saying, AI is remarkable for finance and the promises of this technology, including Big Data, are starting to enter the realms of possibility.
A Crystal Clear Future
A great example in which artificial intelligence has become an especially important asset is effective cash flow forecasting, one of the essential components of treasury which requires a varied skill set. Despite our best intentions, no human being has the cognitive prowess to deliver a fully accurate prediction of the future. As cash forecasting is in most cases still manually managed through spreadsheets, this often results in forecasting errors. Now, technological improvements are quickly reshaping current business strategies. AI algorithms can be of help in this case to complement the human industry expertise and business acumen, while effectively using historic data to paint a more accurate picture.
So how does this work in practice? One possibility is by analyzing a vast amount of data from your ERP and TMS system and attributing certain weights to time-based (day, week, month, …) or amount-based (customer’s payment behavior) parameters. Through variance analysis, the AI system can continuously learn and adapt to new data and expose hidden patterns, making cash flow forecasts become more accurate over time. This can be combined with statistical methods such as linear regression or time-series analysis to create synergies for accuracy.
Not only will AI help with the processing of data, but it will also change the role of the treasury department altogether. Cash forecasting administrators will be better placed to direct their time to the greatest effect, draw out valuable insights from the AI-produced forecast and tailor the process over time to address any variances. The combined intelligence, in which humans and machines have vastly different thought processes, produces superior results. In the future, AI will become as important as the human component for financial decision-making. Collaboration is key, a crystal clear future is the aftermath.
With the help of an AI-based algorithm, a cash forecast with a considerable accuracy can be constructed.
One Night at Time
So Do Androids Dream of Electric Spreadsheets? To dream you need neurons which, until we’ve reached further, are only present in the brain and controlled by the principles of nature. It’s safe to say no blade runner will hunt down your Cash forecasting system (at least for now). But surely the world of finance will be disrupted by the unprecedented AI revolution, one night at a time.
Movie references aside, the better question would be ‘Can my company benefit from this emerging technology?’. Sitting still and letting your corporate competitors gain the first advantage through AI is a risk that every industry should consider for every relevant department involved. For now, we can only know for sure that this unfinished scenario could lead to all sorts of directions. With all these changes happening, you probably do want to be a part of it when the new script on AI in treasury is being written.
Do Androids Dream of Electric Sheep by Philip K. Dick, adapted to the movie Blade Runner in 1984 (yes, it’s that old), ponders the question whether technology can replace humanity in every aspect of life. Whether advanced technology could attain a comprehensive cognitive interpretation of dreaming is a philosophical conundrum that I’ll leave for the brightest among us. However, while this doomsday scenario in Hollywood movies in which robots rise against their human creators is far from happening, the reality is that a computer has already surpassed the level of strategic thinking of a human being. This rise of artificial intelligence carries the potential to disrupt any industry, including treasury, but often leaves you wondering if the hype lives up to reality.
[Spoiler alert] In the dystopian world of Blade Runner, the protagonist called Deckard, a bounty hunter or “blade runner” hunts outlawed androids or “replicants” while feeling no remorse due to them being machines. An interpretation endorsed by the iconic unicorn dream sequence hints that his human memories might have been artificially implanted, implying he might be an android himself. Is this the course artificial intelligence will eventually take us to?
Man vs. Machine – A Boardgame Evolution
In 1997 IBM’s computer Deep Blue beat Gary Kasparov, the world champion at that time, in a game of chess. Deep Blue was able to analyze thousands of high-level chess games that were stacked into its system. When proposed with a move, it would choose the best outcome out of different scenarios. By basic number-crunching it was picking out the move that would lead to the best position on the board. This milestone was heralded as a boon for technology and viewed as almost exclusively disruptive for many industries.
Go, an abstract strategy board game invented in China, has simpler rules than chess, but many more moves at each point in the game. Just to give you an idea, the size of possible outcomes is larger than the number of atoms in the universe. Looking too far ahead in the game, or considering all possible moves and counter-moves is therefore nearly impossible. In 2016, distinguished Go player Lee Sedol was put up for the task to beat the next high-tech invention, named AlphaGo. Created by the sharp minds at Google’s DeepMind, its intelligence is based on its ability to learn from millions of Go positions and moves from previous games. Once again, machine triumphed over its human equivalent when it came to strategic thinking.
AlphaZero, released in 2017, is a version of the same program that takes it a step further. It can play chess, Go and other games and is only given the rules of the game, nothing more. By playing millions of games against itself without any previous knowledge of plays, tactics or strategy, it was able to master these games on its own. So how much time went by from the moment they launched AlphaZero to the moment where it achieved a superhuman level of playing Go? Less than 24 hours. Even more baffling is, while humans have been playing it for the past 2500 years, it came up with brand-new strategies that have never been seen before. While it is ‘only’ about fun and games, this sheds a new light on technological concepts that seemed at first like far-out fiction.
Artificial intelligence systems can dazzle us with their game-playing skills and lately it seems like every week there is a baffling breakthrough in the field with mind blowing results. It is almost unthinkable that the finance sector would be untouched by the rise of AI, any sector for that matter. Nonetheless, with the present hype around it, many of the used concepts and terminology seem to be used carelessly, which makes it hollowed and deprived of any meaning. You have probably heard of the terms “machine learning” and “deep learning”, sometimes used interchangeably with artificial intelligence. As a result, the difference between these concepts becomes very unclear. To understand this distinction and why AI will disrupt current technologies, we have to understand where it comes from.
Let there be l(A)ight – A brief History
Simply put, AI involves machines that can perform tasks that are similar to human tasks. A very broad definition which can go from simple solutions such as automated bank tellers to powerful and complex applications such as androids, which inspired the movie Blade Runner.
Surprisingly, the script on AI arises from a time when James Dean was rocking the screen and Elvis was celebrating his first “Blue Christmas”. While the statistical framework is based on the writings of French Mathematician Legendre from 1805, most AI models are based on technology from the 50’s.
1950, the world-famous Turing test is created by Alan Turing (who will soon be commemorated on the new £50 note). The test reflects on the question whether artificial intelligence is able to appear indistinguishable from a human in terms of thought and behaviour.
1951, the first artificial neural network was created by a team of computer scientists: SNARC (Stochastic Neural Analog Reinforcement Computer). They attempted to replicate the network of nerve cells in a brain. It imitated the behaviour of a rat searching for food in a maze. This was largely an academic enterprise.
In the same way, 1952 rouses the birth of the first computer learning program or machine learning by Arthur Samuel. The program played checkers and improved at the game the more it played. Machine learning, a subset of AI, is defined as the ability to learn without being explicitly programmed what to “think”. It enables computers to learn from their own mistakes and modify to an altering environment. Machine learning also includes other technologies such as deep learning and artificial neural networks. Nowadays this technology can, among other things, use data and statistical analyses to predict possible future scenarios such as for Cash flow forecasting.
The Dartmouth Summer Research Project was a 1956 summer workshop and widely considered to be the starting point of artificial intelligence as a scientific field. With this uprising of technology there came a lot of excitement for the potential of automation in finance and treasury. It was believed to help accountants and bankers speed up their work. But if wishes were horses, beggars would ride. And in this case, androids would be riding along with them. Unfortunately, a reduced interest in the field and many failed projects leave artificial intelligence stranded in what is called the ‘AI winter’.
Luckily humans are not one trick ponies, so our story doesn’t end here. After a period of economic & technological proliferation in the 1980’s, Expert Systems found their way into the world of finance. These are computer systems that are capable of decision-making on the level of a human expert having been designed to solve complex problems. But when push came to shove, the technology wasn’t mature enough and didn’t meet client’s demands.
In 1991 the World Wide Web was opened to the public. It’s the start of the data revolution. In 2005, it reached 1 billion people and today more than half of the world’s population is connected to the internet.
Coming back to our first example, it was in this period (1997) that Deep Blue challenged the capacity of the human brain and proved it could think more strategically than a human being.
Today AI is demanding so much computing power that companies are increasingly turning to the cloud to rent hardware through Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) offerings. That’s why around 2006, players such as Amazon Web Services (AWS) opened up its cloud environment to broaden the capacity of AI even further.
In the same year, Geoffrey Hinton coined the term “deep learning”, helping the progress of operating AI applications in the real world. This brought the world one step closer to bridging the fuzzy gap between humans and androids.
2015, AlphaGo is introduced to the world. Two years later in 2017, its successor AlphaZero sees the light of day.
2019, the first picture of the black hole M87 the constellation Virgo is rendered through artificial intelligence opening the door to new knowledge in the universe. The path of AI took us a giant leap forward, but we’re far from the finishing line. Roughly 90% of the universe exists of dark matter or dark energy that leaves us in confusion. Accordingly, a similar percentage of untapped dark data, the fundamental building block to understand a company’s future, isn’t being used.
We are very proud at our partner Cashforce. What a year it has been for Cashforce! From opening new offices, to processing millions of transactions, Cashforce successfully round up 2019. More specifically last year, Cashforce:
- Opened up three new offices in London, Copenhagen and Ghent
- Moved its HeadQuarters to Antwerp, Belgium
- Visited over 20 countries during 2019
- Attended 12 Treasury conferences, gave 9 speaking sessions, hosted 4 Belgian Beer Nights and gave away 2159 Chocolates
- Processed over 40 million transactions, doubled their clients, hired 14 new FTE’s and collected $5 million of investments by Citi & Inkef
- Gained 724 followers on social media, consumed 10,498 cups of coffee and held 5 board game nights
- Won the best use of Artificial Intelligence in Treasury Management reward by Global Finance Magazine 2019 and became 1 of the Top 5 hottest Startups in Belgium
- Settled new partnerships with Citi, BNP Paribas and KBC
- Upgraded their Smart Algorithms and further developed Artificial Intelligence
For many multinational corporations, effectively managing their working capital across numerous regions can be a significant challenge. Additionally, optimizing cash streams in a complex data environment can be a time-consuming process. The same issue goes for Dawn Foods, a global B2B bakery ingredient supplier with multiple entities & finance departments. With more than 50 locations worldwide, serving products in 106 countries and 40.000 customers served globally it is one of the main players in the food industry.
Starting 2015 the company started a change management process to turn Dawn Foods into a more cash orientated company. A taskforce was created supported by Bart Messing, European Treasury Manager and Marc Kersten, European IT director, sponsored by the VP Finance & IT Michael Calfee.
Their key objective was a 10% year-over-year reduction of Net Working Capital Days.
One of the essential building blocks of this plan was implementing a 24/7 working capital tool whereby the KPI’s could be reported into several dimensions that are relevant to the different business units and functions. The different dimensions are important, as the business will only support improvement processes and accept targets unless the KPI’s are measured in relevant dimensions.
After careful comparison based on an extensive survey under key business people between internal/external tools on quality requirements, costs and potential benefits, Cashforce, a ‘next-generation’ cash & working capital analytics solution, came out on top. By designing a proof of concept, in cooperation with the internal IT department, a successful solution was reached. After the implementation the results were already significant in a short time: an instant working capital dashboard that provides 24/7 insights, as well as with simulations in different dimensions that are relevant for each department.
By providing the right technology, in combination with an unmatched cross-departmental cooperation, Dawn Foods was able to build a bridge between its finance department and the rest of the departments, thus reducing complexity and increasing visibility and insights.
This led to millions of dollars saved since setting up the new project (over a three-year period). The cash that was freed up has in the meantime been used to finance a strategic acquisition.