| 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.
| 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.
| 03-03-2020 | treasuryXL | Cashforce |
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.
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 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.
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.
| 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.
[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?
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.
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’.
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.
| 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:
| 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.
| 10-12-2019 | treasuryXL | Cashforce |
One would imagine that in a world where smart cities and virtual reality are becoming a part of daily reality, treasury and finance departments would have perfected their cash forecasting by now, giving the CFO a level of confidence in the numbers. Surprisingly, that doesn’t appear to be the case at all – both PwC’s & Deloitte’s Global Benchmarking studies highlighted cash and liquidity risk as the most important treasury challenge to manage.
If you look closer, it’s not difficult to see why: try managing and forecasting the cash flows of a complex internal ecosystem of multiple ERP’s, FX exposure and geographic entities, combined with increased global uncertainty, tax changes, interest rate rises, and regulatory change. Still, having an accurate cash flow forecast and understanding the underlying drivers is essential to a company’s well-being, as it can help you foresee potential problems which may arise in the year ahead. A lot of companies around the world are therefore increasing their efforts when it comes to cash flow forecasting, but with variable results and accuracy.
So what sets good cash forecasting (i.e. accurate and efficient forecasting) apart from bad cash forecasting (i.e. not transparent, inaccurate & time-consuming cash forecasting)?
A lot of Corporate Treasurers are seeking an accurate cash forecast through a delicate combination of well-chosen cash flow drivers & assumptions. But, to what extent do they have a good view of these cash flow drivers? Do they know what is really eating and feeding their cash (more than the typical high-level AR, AP, Treasury flows that your Treasury Management System will consolidate)?
There isn’t a lot of visibility, unfortunately. Why is that? The classic TMS will typically consolidate basic forecasted flows from the different OpCo’s. The problem is that these OpCo’s cash forecasts are already consolidated from the underlying business transactions. This blurs the insight in the real cash flow drivers and gives no assurance whatsoever on the quality of the data.
To build a good forecast, it is important to have clear and error-free access to the underlying business transactions. In a recent PwC study, only 6% of respondents said they made use of the inputs at the transactional level. But thanks to advances in technology, particularly big data analytics, treasurers can have instant access to the details of the underlying cash movements and are given the ability to drill down to the transaction level. In the gif below, you can see what this means in practice.
Suppose you want to know exactly what drives your company’s cash flow in a certain period. The GIF below demonstrates how easy this could be, using the right platform. Via an easy-to-use click-through interface, the user is able to gain insights per month, quarter, week and day including instant access to the transaction level details.
Cash flow forecasting is often associated with a pile of Excel sheets and manual work. Treasurers are forced to turn to Excel to calculate their forecasts, because classic Treasury Management Systems do not offer the required flexibility.
Getting insights into all your OpCo’s cash flow drivers is one thing but combining all these data sources and applying the right logic/rules to generate a good forecast is another. Let’s take the example of applying vendor payment behavior. Intuitively, it makes sense to enrich invoicing & sales order details with data on when vendors actually pay. Many companies, however, struggle to take this data into account. In general, they haven’t set up the appropriate algorithms to include in their forecasts. Hence, they face inaccurate forecasts and a lot of time is spent explaining (over and over again) why it was inaccurate.
Defining forecasting logic in a smart way is not an easy challenge. Yet, if your goal is to achieve an accurate forecast, a set of smart logic algorithms is invaluable. Again, modern technology proves to be a great asset. Progressive companies are using technology-driven, smart engines to calculate & automate their cash forecasts, taking over the manually intensive work and proposing logic that could improve the forecast in the future.
Above you can see how a smart engine works in practice. Cash flows are projected into the future (blue line) using forecasting logic. The dotted orange line represents a scenario with one or more of the underlying assumptions changed and immediately shows the impact relative to the blue line.
Even if your forecast is no less than a piece of art, it might be underused, or not used at all. To make a real impact, there should be actions retrieved from the forecast results. There is a lot of potential in accurately predicting what might happen in the future and this potential should be translated into value.
There is even more value in considering multiple scenarios by changing some of the underlying assumptions (e.g. changing the day or frequency of your payment runs). When working in Excel or a TMS, changing assumptions might trigger a lot of additional manual work and is unfortunately often avoided. To get the most out of your forecasting process, it makes sense to build multiple forecasts and assess the impact of each of these scenarios on cash optimization. Driving action combined with building multiple scenarios, can transform finance departments into business partners for fueling a company’s growth.
The orange line reflects a scenario, built by the user. These views give her/him an immediate comparison between the current forecast (full blue line) and a different scenario (based on assumptions made by the user). A powerful simulation engine is able to show the impact of different scenarios in a blink. Imagine the power this can bring to a business-driven finance department.
Mark O’Toole heads up the Americas for Cashforce, a big data analytics & TMS technology provider focused on cash management, forecasting and working capital.
| 18-10-2019 | treasuryXL | Cashforce |
Cashforce, a Fintech leader in Cash forecasting & Working capital management, announced that it has closed € 5 million in Series A funding. The growth financing round was led by INKEF Capital and Citi Ventures. The existing investors Pamica NV, the investment company of Michel Akkermans, and Volta Ventures, are co-investing and reinforcing their commitment to the company.
Since 2018, Cashforce has demonstrated hyper-growth by developing multiple partnerships and by streamlining Cash forecasting processes & Working capital management for enterprise customers globally. New offices have been opened in London, Ghent and Copenhagen in 2019, with others (Zurich, Singapore…) to follow soon.
This funding round will accelerate global growth and presence in new markets.
“With the help of Cashforce’s technology, the way cash flow forecasts are generated and Working capital is managed can be radically transformed. By addressing these deep-seated challenges for many corporates using automation and AI, Cashforce is well-positioned and has tremendous potential to significantly help enterprises,” commented Corné Jansen, Managing Director of INKEF Capital.
”There is an increasing appetite in corporate treasury for integrated decision support tools from their banks for the next investment, fund or hedge action going beyond what their existing systems can provide today. As a prerequisite step to delivering such solutions from Citi, we look forward to collaborating with Cashforce to significantly improve our clients’ ability to aggregate disparate data sets across their enterprise to help better manage their working capital and more accurately predict through algorithmic techniques their potential liquidity exposure. At Citi, we are running a number of experiments collaborating with our clients and fintechs – such as CashForce – empowering our clients’ journey towards Smart Treasury. This journey moves them beyond descriptive analytics to decision support and decision automation, offering the opportunity to realise the promise of full automation of operational treasury,” said Ron Chakravarti, Citi Managing Director, Global Head – Treasury Advisory.
Executive Chairman Michel Akkermans and CEO Nicolas Christiaen stated: “Cash forecasting still remains one of the most important challenges for treasurers worldwide. The last three years have been very fruitful for us, developing our solution and broadening our eco-system through partnerships with global banks, treasury consultants and bank connectivity partners. Our mission remains unchanged: delivering reliable technology that enables financial leaders to make high-caliber decisions. We are therefore very enthusiastic about our new global strategic banking partnership with Citi, jointly offering their corporate clients a crystal-clear future.”
INKEF Capital is an Amsterdam-based venture capital firm that focuses on long-term collaboration and active support of innovative technology companies. INKEF Capital was founded in 2010 by Dutch pension fund ABP and with €500 million under management is one of the largest venture capital funds in the Netherlands. INKEF focuses on investment opportunities in Healthcare, Technology, IT/New Media & FinTech.
Citi Ventures ignites change and reimagines solutions that drive economic progress for clients. Headquartered in Silicon Valley with offices in San Francisco, New York, London and Tel Aviv, Citi Ventures accelerates discovery of new sources of value by exploring, incubating and investing in new ideas, in partnership with Citi colleagues, our clients, and the innovation ecosystem.
Pamica is the investment company of Michel Akkermans, is a serial entrepreneur in Fintech companies. Amongst others, he was the Chairman and CEO of successful companies such as FICS and Clear2Pay. After the global payment solution company Clear2Pay was acquired by FIS in 2014, he became an active investor and board member in several companies and private equity organizations, as well as a venture partner and Chairman of Volta Ventures.
Volta Ventures Arkiv invests in young and ambitious internet and software companies in the Benelux. The fund has € 55 million under management and is supported by EIF and PMV.
| 26-09-2019 | treasuryXL | Cashforce |
Deriving meaningful information from extremely large volumes of data from multiple sources is time-consuming and inefficient for any finance or treasury function; whether that be to provide financial data or forecasts to the market, banks or internal stakeholders, the challenges are myriad. But the department cannot forecast without that insight.
To compound the problem, in a world where volatility and uncertainty have become the norm, treasurers are now part of their organisation’s strategic leadership and must increasingly find ways of bolstering their approach to gain a much-needed competitive edge.
This article considers three of the most common challenges for finance and treasury departments today, and explores how the Cashforce platform solves them:
Like many other departments within a business, most treasury functions have large volumes of consolidated data in complex spreadsheets, very rarely providing easy access to transactional data. Decision making is difficult as the answers are often buried in complicated formulas and countless links to excel templates. The problems caused by an inability to identify relevant data are compounded by any number of missed opportunities and risks. To put the big data problem into perspective, a report from McKinsey & Company suggests that a typical organisation uses less than 1% of the collected data to make decisions.
“A typical organisation uses less than 1% of the collected data to make decisions”
A major British retailer faced this very challenge — large volumes of data embedded in 10 different ERPs and no consolidated view on what was really tied up in working capital. To unlock the potential that already existed within the retailer’s own data, they asked Cashforce to implement a cloud-based solution with detailed dashboards to drill down from a consolidated position to core data by integrating with ERP systems. Within three weeks, this opened up over 20 million transactions per month, ready for analysis.
Cashforce‘s big data engine accesses vast volumes of data quickly and easily via a library of APIs and connectors which take raw data from multiple sources (including ERPs, Treasury Management Systems, data warehouses and banking platforms) and transforms it into meaningful, easy to understand dashboards — empowering the user with the big picture.
If cash is king, then accuracy in cash forecasting is the prodigal son. PwC‘s 2017 and 2019 Global Corporate Treasury Survey shows how forecasting accuracy is key to managing and running a business efficiently, and it continues to be a high ranking C-suite priority. A lack of transparency over data means that output from generic treasury management systems inaccurate and unfocused. To maximise predictive, trend-based behaviour you need access to the raw data. But how?
Far from the futuristic concepts, they were perceived to be, machine learning and artificial intelligence are being deployed right now, with stunning results. Smart algorithms are providing proactive optimisation actions to generate highly accurate forecasts, and intelligent simulation engines enable companies to consider multiple scenarios and measure their impact. Cashforce is unique in that the platform can be set up quickly, even in the most complex environments, seamlessly connecting with any ERP system. As a result, finance departments can be turned into business catalysts for cash generation opportunities throughout the company.
“If cash is king, then accuracy in cash forecasting is the prodigal son”
In the case of education company Pearson, CFO James Kelly was looking to improve the cash forecasting abilities of a TMS that was the equivalent of an Excel spreadsheet.
“If you don’t have predictability, you can end up overriding your forecast and saying ‘nine days out of ten I’m spot on, but there’s the risk that one day out of ten I’ll be miles out’ – so you decide to hold a lot of cash back just in case,” Kelly said.
Pearson partnered with Cashforce to deploy an AI-supported forecasting solution which integrated with the group’s systems, replaced manual keying with robotics, and provided multiple AI algorithms offering unprecedented insights into cash flow. AI-based forecasting unlocked significant amounts of trapped cash overseas, and balances were reduced by over £100 million — freeing up cash to invest elsewhere in the business instead of drawing down on credit facilities.
When it comes to the universal challenge of onboarding, the focus must be on simplification and streamlining. Central to this is the alignment of a library of connectors to data sources. This is why Cashforce’s working capital module integrates with multiple ERPs to provide granular detail within operational transactional data. And because the user organisation may be running different or multiple ERPs in different regions, we recommend an ERP-agnostic solution.
The operational data in an ERP only provides half a story so our solution also sits on top of treasury systems to provide a holistic cash flow forecast combining both treasury and operations with data based on a client’s unique reporting requirements.
End-user flexibility is a key feature of any financial system today, so user roles can be defined and users added or removed by a client administrator. The additional benefit of a SAAS platform means no heavy lifting is required by your IT department.
“With Cashforce, finance departments can be turned into business catalysts for cash generation”
In the course of a recent implementation, British manufacturer was faced with the challenge of Brexit-related contingency planning, when it decided to stockpile certain FDA-approved products destined for the US market. The firm’s initial focus was on cash management and forecasting but refocused mid-way on working capital management with a major focus on inventory and traceability. Such a change in scope can often lead to significant delays in delivery, but with Cashforce driving the process, the project was delivered on time.
Cashforce is a smart cash flow management and cash flow forecasting platform for working capital intensive businesses. Our technology is helping Finance departments save time and money by offering cash visibility & pro-active cash saving insights. CFOs and Finance departments can drill down to the cash flow drivers and smart algorithms are applied providing pro-active optimization actions. An intelligent simulation engine enables companies to consider multiple scenarios and measure their impact. As a result, finance departments can be turned into business catalysts for cash generation opportunities throughout the company.
Cashforce is unique because it offers full transparency into what exactly drives the cash flow of complex (multinational, multi-bank, multi-currency, complex ERP(s)) enterprises, typically with revenues between € 50 million and € 10 billion. It is the first cash management platform that builds a bridge between the treasury department and the actual business departments such as sales, logistics and purchasing. Unlike other enterprise software players, the Cashforce platform can be piloted within a few hours in complex environments, seamlessly connecting with any ERP system.
Currently users in over 40 countries are using our platform to streamline their cash management processes. Cashforce has proven its value in various complex environments, including environments where in-house banking, cash pooling, POBO, ROBO, etc. are used.
Cashforce is headquartered in Belgium with an office in New York City, serving customers such as Hyundai, Portucel, Alcadis among many others worldwide.
| 12-09-2019 | treasuryXL | Cashforce |
Is your company struggling with liquidity forecasting?
Find out how you can transform your forecasts from bad to best.
Too much manual effort and too little time for analysis, a statement (too) many treasurers can relate to. According to PwC and their Global Treasury Benchmark Survey, still 87% of treasurers use technology from the 1980s (i.e. spreadsheets) or have a disparate set of ERP systems, multiple bank websites and email. Consequentially, this leads to a lack of visibility and makes it very arduous to answer critical questions like “Is my company over borrowed, underinvested or overexposed?”.
An inability to answer this question not only constrains treasury’s ability to measure its success but could harm the future viability of the company. With automated and accurate forecasts & simulations within reach, this is a clearly avoidable risk.
During this one hour webinar, Bruce Lynn of the FECG and Nicolas Christiaen from Cashforce discuss how to radically optimize your cash forecasting workflows by:
About Cashforce
Cashforce is a ‘next-generation’ digital Cash Forecasting & Treasury Platform, focused on analytics, automation and integration. Cashforce connects the Treasury department with other finance / business departments by offering full transparency into its cash flow drivers, accurate & automated cash flow forecasting and working capital analytics. The platform is unique in its category because of the seamless integration with numerous ERPs & banking systems, the ability to drill down to transaction level details, and the intelligent AI-based simulation engine that enables multiple cash flow scenarios, forecasts & impact analysis.
Cashforce is a global company with offices in New York, Antwerp, Amsterdam, Paris & London and provides Cash visibility to multinational corporates across various industries in over 120 countries worldwide.
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