Global digital transformation has kick-started the Fourth Industrial Revolution, also known as Industry 4.0. For corporate treasurers, this represents a complete shift in approach to treasury processes, technology and, most importantly, talent.

    HSBC’s recent Global Liquidity and Cash Management Forum, held in Dublin, Ireland, highlighted the technological advances that are revolutionising the financial services landscape around the world. For treasurers, these developments offer exciting opportunities to enhance efficiencies, but also present challenges in dealing with new technology providers, and securing businesses from cyber threats.

    Alan Duffy, CEO of HSBC Ireland, gave the welcome address. He began by noting the excitement around technologies such as machine learning and artificial intelligence (AI), and how the financial sector is keen to find practical applications of such technologies. In an environment where interest rates are low and will likely remain so for some time, it is incumbent on financial professionals to manage cash more efficiently and to make sure that a return on capital can be achieved more efficiently, he said. Technology is a key enabler in these areas.

    Duffy also noted that while neural networks have been around since the 1950s, the big game-changer today is the amount of data in circulation, as well as the availability of open source software and increased processing speeds. This enables data to be used and interpreted more effectively. Duffy said the largest spenders on AI and machine learning are banks, and the technology that they are investing in has to be deployed to enable treasurers to manage their function more effectively. He cited the launch of HSBC’s next generation virtual accounts (ngVAM) as a practical example of this, reducing the multiplicity of physical bank accounts that corporates might have, making their jobs easier.

    The era of Treasury 4.0

    Following Duffy’s welcome address, a Senior HSBC representative took to the stage. He explained the context of how treasury is evolving as the world enters the fourth industrial revolution.

    The good news for treasurers, he said, is that the new treasury reality, Treasury 4.0, is bringing several innovations to transform the landscape – and prompting new regulations, such as the General Data Protection Regulation (GDPR) and the updated Payment Services Directive (PSD2), which are revolutionising the way data is collected, stored and distributed. The instant payments landscape is also bringing further opportunities for innovations that corporate treasurers might wish to take advantage of, such as Request to Pay and Swift gpi.

    Despite the digital opportunities, cybersecurity has become a risk management imperative. Companies need to continually invest in the latest technology protocols and firewalls to protect their devices, their networks and their reputations. For treasurers, this means ensuring that companies’ cash balances are kept safe from cyber criminals.

    They also warned that treasurers must look beyond the hype of Treasury 4.0 towards practical, real-world applications of disruptive technologies. He cited HSBC’s Liquidity Investment Solutions Portal, called LIS, as an example of this type of technology in action. It enables treasury functions to invest surplus cash automatically according to predefined parameters, which he noted was a powerful tool for the real world of Treasury 4.0.

    The power of data

    Peter Simon, Lead Data Scientist at DataRobot was next to address the event. DataRobot is a leader in automated machine learning, with a platform that enables data scientists to be exponentially more productive but also allows other users, those outside the traditional machine learning audience, to easily build and deploy sophisticated, robust machine learning models.

    A typical AI system is made up of multiple components. Alongside database’s and rules-based decision systems, the bulk of a typical AI set-up often consists of machine learning models.

    There are two main types of machine learning. The first is ‘supervised learning’, which accounts for some 80 per cent of machine learning applications in business. Supervised machine learning is about building models using historical data with known outcomes and using these models to predict what will happen with incoming records. In a given body of data, each row will represent a record, event or item, e.g. a transaction, a customer account, an invoice, or an order. Each record will have various fields (or columns) associated with it. For example, for a transaction, there might be a column for the payment amount, a column for the payee, some columns detailing information on the payee or the products sold, and so on. Alongside that sits the “target variable”: an outcome that the user wants to predict—was the payment fraudulent, did the payment arrive successfully, was there an operational failure in there, did I sell a product to a particular customer, for example?

    The other type of machine learning is ‘unsupervised’. This is where the user doesn’t have an outcome to predict; rather the data itself is being examined for similarities and differences. Combinations of supervised and unsupervised machine learning can be very powerful (for instance, in fraud applications, where the supervised learning can detect known patterns of fraudulent behavior, augmented by unsupervised learning to detect anomalies which may be unfamiliar).

    At the highest level, three things are required for machine learning:

    1. Things to predict — accounts, events, customers, transactions, cases and so on.
    2. Data describing these things — do you have (or can you source) data which describes the things you want to predict?
    3. Business value — is the ability to predict likely outcomes of the incoming data, etc, valuable to the business; or, would having a good model of the behaviours described by the data be of strategic value? Some machine learning use cases are philosophically very interesting, but don’t produce anything of tangible value.

    Simon believes that businesses in any industry can benefit from AI and machine learning, with the technology’s potential spanning many different functions in businesses. Machine learning has numerous applications in corporate treasury, such as fraud prevention, setting credit terms, predicting invoice payment times, matching payments and invoices, predicting the funding environment, right up to holistically forecasting cash balances and working capital requirements.

    Simon cited a case study of a customer who is a supply chain outsourcer. They needed to forecast when they would receive payment for each invoice, which would allow them to facilitate the processing of collections and better plan their cash flow. The company’s invoices were a good starting data set, with the details of the products on the invoices pointing to differing payment discipline patterns from customers. Past customer behaviour was also important, as was more general information on the customers. For example, if a customer always pays 30-day invoices after 90 days, this needs to be captured in the machine learning model’s training data so it can learn from that pattern. Simon noted that the solution for this client actually took less than a week to build and didn’t require specialist data science knowledge.

    Another DataRobot customer addressed a similar problem in a very different way, predicting cash inflows in aggregate. This new process allowed the client to move away from funding their business solely on the basis of ‘cash on hand’, allowing for a much more accurate and detailed picture of what cash inflows to expect on which date. This reduced the need for short-term funding instruments substantially, reducing the company’s funding costs by USD3-5 million a year, thanks to a model that took just a few days to build.

    Simon went on to highlight fraud prevention as another use case. In any process where business rules are used to flag something as potentially fraudulent and someone has to manually check whether such alerts are actually a fraud or not, the team checking these alerts are effectively building a training data set for machine learning. This can be used to build machine learning models which can be put on top of the rules-based system, typically reducing the ‘false positive’ rate by some 60-80 per cent and therefore creating substantial efficiencies in this area.

    Simon highlighted that with modern automated machine learning (autoML) platforms such as DataRobot’s, it is no longer necessary to have deep machine learning or data science expertise to build such powerful models. Treasury analysts, and business intelligence analysts more broadly, bring two vital elements to building machine learning and AI models. First, they understand the business, which is crucial – since there is a need to understand the business problem at hand and to “know what questions to ask”. The other important element is their deep understanding of the business’s data. As such analysts typically spend much of their time on reporting and building dashboards to capture a point-in-time or historical snapshot of the business, they already know where the required data lives. AutoML gives them the ability to take the same data and build powerful predictive models; the software deals with the maths, programming and computation, allowing users to focus on adding value in their areas of expertise. At the same time, automated machine learning also allows existing data science teams to exponentially increase their productivity.

    Despite the digital opportunities, cybersecurity has become a risk management imperative.

    Treasury 4.0 in practice

    The Forum concluded with a panel of treasury experts discussing what Treasury 4.0 means to them in real terms today. The panel comprised John Rice, Financial Services Director at An Post; Catherine O’Brien, Corporate Treasury Professional; Kevin Daly, Assistant Treasurer at James Hardie International Finance, and Vice President of the Irish Association of Corporate Treasurers (IACT); and Emanuele Vignoli, Managing Director, Global Liquidity and Cash Management Europe. The session was moderated by Cara Savas, European Head of Sales, Global Liquidity and Cash Management, HSBC.

    Vignoli observed that Treasury 4.0 is an opportunity for treasurers to refocus activities on their core business by further automating areas such as reconciliation or payments. There are also hidden efficiencies for treasurers to garner in technologies such as biometrics. Not only can this help reduce cybercrime, it can also be used to optimise processes by eliminating the need for physical tokens or passwords.

    Vignoli’s final opening thought focused on the creation of a data lake that a number of treasuries are moving towards, which will provide much greater visibility around global flows. Treasurers, he said, will be able to operate by using a different set of data, which will enable them to manage their business in a far more forward-looking manner.

    Rice then outlined An Post’s three-year digitisation process, which includes a focus on two main aspects – customer experience and efficiency. He explained how the company is using the digital tools it has to try to understand and map all the different transactions and all of the digital ‘journeys.’

    One best practice Rice shared in terms of the digital journey is to imagine what you actually want and, more importantly, imagine what the customer wants, because the technology is there. Once you understand what you can really do with your data, through the connectivity offered by open application programming interfaces (APIs), for example, the world is your oyster, he said.

    Daly then described the technology journey taking place at James Hardie. While not in the AI/robotics phase, he said the business has been in discussions with a fintech in a bid to use technology as an interface between its different banking platforms and to consolidate some of that to create more efficiency in the treasury function.

    He said one of the big challenges is the reliability and security around fintechs. Corporates are used to dealing with banks, whereas fintechs typically represent a step into the unknown. However, the more that fintechs and banks work together, the more reassurance corporates will take from that.

    Industry 4.0 brings the physical and digital worlds together, O’Brien commented, particularly with products such as virtual accounts. She had previously been at a company where they were undertaking a cash management request for proposal (RFP). One possibility that became compelling to her treasury was the use of a virtual account management structure where ideally one physical bank account would be switched on for the company 24/7, while everything else would be a data account. It is possible, she explained, to get down to one bank account number per general ledger (GL) code. This visibility of data on a real-time basis is extremely useful to treasurers and colleagues in relation to the ERP and the GL, she said.

    Turning to cybersecurity, Rice said that given his organisation is entrusted with managing customers’ funds, the business must comply with global standards. He described these as the baseline in terms of the company’s cybersecurity investments.

    Daly commented that his company has lived and breathed cybersecurity for the past four years since a dedicated global head of security was appointed. Now, a month doesn’t go by without some element of security or compliance test or learning – it is ingrained in the business across the entire group, including treasury.

    This point was broadened to a wider discussion about people and talent in the business in an Industry 4.0/Treasury 4.0 environment. Vignoli mentioned an HSBC team in London that focuses on integration. Once a deal is won, this team work closely with the clients to integrate their ERP system with HSBC’s own internal systems using host-to-host connectivity. As such, the bank ensured that this entire team was trained in new technology, such as open APIs.

    Daly noted that, as most corporate treasury teams are relatively small, they have to use the resources available as best as they can. He advised utilising team members’ aptitudes for technology, even if they are lacking in formal qualifications or pure IT skills, to help implement new processes. This, he said, would add to the treasury function and the business as a whole.

    Conversation then returned to fintechs. Rice advised attendees to thoroughly research any fintech they are thinking of working with. He described how An Post had looked at a payment interface, which seemed – on the surface – to be solid and delivered everything they needed. However, he stepped away from implementing it after a peer told him the reliability and security was not at the level An Post required. Rice also noted that more insightful information is likely to be gained by talking to a wider peer group than from reading customer references supplied by fintechs.

    O’Brien also supported this approach, adding that she came across possible solutions to pain points during her former company’s digitisation journey by talking to peers and vendors at industry conferences.

    Vignoli commented that treasurers shouldn’t be shy in asking for advice or help from banking partners or colleagues in the corporate space. Most banks will have dealt with multiple fintechs. They will be able to share data, some of the outputs, some of the risk frameworks, and some of the business cases. This additional external perspective is another source of information that treasurers can use as part of their decisionmaking process.

    The final note of the event highlighted how treasurers could move forward into the Treasury 4.0 era. Daly suggested treasurers pick one or two initiatives every year and set out to achieve them in the next 12 months. By doing that annually over three to five years, treasury will be in a much better place and treasurers will be more efficient users of technology, he concluded.


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