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Press release | New Cashforce office in Warsaw, Poland fuels its cash forecasting product excellence

08-06-2021 | treasuryXL | Cashforce

Cashforce, an innovative Cash Forecasting and Working Capital Analytics solution provider, has announced it will open a new office in Warsaw, Poland to expand the company’s activities. This new office will further accelerate Cashforce’s growth and market presence on a global scale with the addition of a new technical team. For this expansion, various product-focused positions are becoming available, ranging from Chief Architect and team leaders to analysts and front-end/back-end engineers.

“We are delighted to share the news of the opening of our product-focused branch. Together with our new platform which launches soon, we continue to push the frontier of Cash Forecasting & Working Capital product excellence,” said Nicolas Christiaen, CEO of Cashforce.

Cash forecasting remains a top priority of any corporate and Cashforce offers a unique solution to assist finance and treasury departments in this challenge. Building upon years of experience, we’re reinforcing the vision to save time and cash by offering automated Cash Forecasting technology. Our new platform is equipped with real-time data processing capabilities, an intuitive user-experience that lowers the barrier to entry and enhanced (AI-powered) scenario building capabilities.

Jan Bakker, COO of Cashforce, adds: “It’s great to see our global presence ramping up. By reinforcing the product team with various technical positions, we’re ready to further integrate the latest and greatest technologies into our product.”

As a Fintech scale-up disrupting the treasury space, we are experiencing substantial international growth. To make our ambitious vision a reality, we are looking for motivated candidates to join us in creating a world-class product. Of course, it all starts with the amazing people. You can become a part of a dynamic and global team that encourages ownership, diversity and personal growth. Learn more about our company culture here.

WANT TO BE A PART OF OUR FINTECH EXPERIENCE? FIND OUT MORE ON OUR OPEN POSITIONS HERE.

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Cashforce is a ‘next-generation’ Cash Forecasting & Working Capital Analytics solution, focused on automation and integration. Our cloud-based software enables corporates to unlock their data and create smarter decisions, saving time and money. By integrating internal & external company data (ERPs, TMS, data lakes etc) and processing them through machine learning techniques, our SaaS solution provides insight into Cash Flows & Working Capital, automates manual and cumbersome Treasury tasks and enables AI-powered-scenarios. Cashforce is used by midsize and large corporates and has users in over 120 countries.

When Cash is key, Cash Forecasting and Working Capital leads your company towards a crystal-clear future. When Cash is key … Cashforce.

 

Press contact
Benjamin Bergers
benjamin.bergers@cashforce.com
+32 (0) 479 66 27 21

 

Texpo Webinar | Dark Data- Search, Collate, Conquer – Making Sense of Unstructured Cash Forecasting Data

| 08-06-2021 | treasuryXL | Cashforce

Our Partner Cashforce is holding a webinar hosted by Texpo, in which the topic ‘Dark Data: Search, Collate, Conquer – Making Sense of Unstructured Cash Forecasting Data’  is presented together with  David Jacoboski, CTP (Drew Marine).  Dark data is defined as unused or hidden data from relevant departmants in your business, which might have intrinsic value. Watch the full webinar below 👇🏽

 

Overcoming Resistance | Integrating Data in Cash Flow Forecasting

| 17-05-2021 | treasuryXL | Cashforce |

Treasurers at mid-cap Corporates looking to use large-scale data analysis to enhance cash flow forecasting are finding colleagues hesitant.

The advantages of using sophisticated data analysis in cash flow forecasting are clear to a growing number of treasurers intent on improving accuracy and eliminating human error. But implementing and executing a data-driven approach often requires collaboration with teams outside treasury, such as AR and credit collections—and some NeuGroup members are meeting resistance.

  • Solid support from leadership and showing the benefits of data analysis may make the transition smoother and help get members of other teams on board.
  • That key insight emerged from a recent discussion at a meeting of NeuGroup for Mid-Cap Treasurers, sparked by a presentation about data-enhanced cash flow forecasting from Cashforce. Read an earlier article from Neugroup here.
  • “A data mindset requires an analytical filter,” one member said, and if another team does not thrive on data, it takes some effort to get colleagues to buy in.

Overcoming intimidation. “I like to be very data-driven,” one member said. “Sometimes that doesn’t go over well in our company. It can be intimidating to people.”

  • “When you start questioning trends, it doesn’t always make people feel very good,” she continued. “I think there can be a lot of defensiveness.”
  • Another treasurer said that, in his experience, “having access to data and showing it to [staff] kind of scares them. People say they want to change—people don’t want to change.”
  • Though there can be a learning and implementation period, he said he was able to find success by stressing how much time data analysis could save in the long run.

Navigating collaboration. Some members said teams that consistently set low expectations for cash flow are often obstacles to using data that produces different, more accurate forecasts. “There can be sandbagging in the forecast, people can be resistant to being more optimistic,” one member said.

  • Another said that, though she would like to see the company implement a more data-focused model for cash flow, it would be too great a challenge to work with functions that don’t fit under the treasurer and do not share the data mindset.
  • One treasurer said his company is having these issues with its AR team, which does not report to him. “When you compare quarters, [we are] 10-15% over our forecast,” he said. “There’s a disconnect.”

Teamwork, dream work. That member said he was able to work with his company’s AR team to incorporate data and effectively eliminate the issue, though there was initial reluctance.

  • He recommends a single individual in a management role spearhead this kind of change. “If it is more driven by one leader, it is easier to shield criticism and make a right decision.”
  • The member said another source of friction can be FP&A and other finance or business leaders outside of treasury who want to maintain oversight of forecasts.
  • Though there is value in working together to incorporate data for forecasting, he said, “the entire organization needs to be ready to become more objective rather than try to manage divisions.”

 

Webinar Recording: The importance of cash management during the crisis | the impact of the pandemic

| 31-03-2021 | treasuryXL | Cashforce |ACT

Rewatch ACT’s session ‘The importance of Cash management during the crisis: the impact of the Pandemic’ with David Shinkins (Barclays), James Marshall (Virgin Media), Hailey Laverty Hotels & Resorts) & Nicolas Christiaen from our Partner Cashforce.

 

 

 

Webinar recording: Cashforce & TIS are Partnering Up to Deliver Best-of-Breed Technology

| 17-03-2021 | treasuryXL | Cashforce | TIS

Cashforce & TIS are partnering up to deliver best-of-breed technology. Watch the webinar recording with Nicolas Christiaen and Joerg Wiemer and get to know more about this best-of-breed approach and how this partnership can help you tackle your challenges in cash flow forecasting and corporate payments.

 

 

 

Cashforce & TIS – Partnering Up to Deliver Best-of-Breed Technology

| 29-01-2021 | treasuryXL |

In July 2020, Cashforce, the “next generation” cash forecasting & working capital analytics company and TIS, well known as a leading bank connectivity & payments provider formed a strategic alliance. This collaboration provides a unique solution for corporates looking for a rich cash forecasting and payment experience with seamless integration to their banks and enterprise systems (ERP, TMS etc.).

Join the webinar with Nicolas Christiaen, CEO & Co-founder at Cashforce and Jörg Wiemer, CSO and Co-founder at TIS and get to know more about this best-of-breed approach and how this partnership can help you tackle your challenges in cash forecasting and corporate payments.

Register Here

 

Date and Time
  • Tuesday, March 2nd 2021
  • 16:00-17:00

 

 

 

Do Androids dream of Electric Spreadsheets? A Beginner’s Guide to AI in Treasury – Part II

| 03-03-2020 | treasuryXL | Cashforce |

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.

Grim city

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.

Electric 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.

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Do Androids dream of Electric Spreadsheets? A Beginner’s Guide to AI in Treasury – Part I

| 24-02-2020 | treasuryXL | Cashforce |

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.

Abstract blue lights background. Vector illustration, contains transparencies, gradients and effects.

[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.

SNARC computer

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’.

Blade runnerinfographic artifical intelligence

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.

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Impressive year for our partner Cashforce

| 28-1-2020 | treasuryXL | Cashforce |

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

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From a P&L to a Cash-driven organization in less than a year after implementing Cashforce

| 7-1-2020 | treasuryXL | Cashforce |

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.