There’s no escaping the fact that KYC is an area of concern for corporate treasury teams. We ask Gert Sylvest, co-Founder of Tradeshift and GM of Tradeshift Frontiers, for his thoughts on how treasurers can ensure they are dealing with the issues correctly through effective processes and how technology can help.
“When it comes to struggles, access to information is a significant issue for treasurers. The department’s financial and insurance counterparts are working off balance sheets, quarterly or yearly assessments. Their insights aren’t based on day-to-day business outputs and processes. This leads to a lack of understanding around the real risks that the ever-changing business landscape presents.
“There’s also a real knowledge gap around understanding how a company spends money. The problem is particularly acute when it comes to comprehending spending patterns and locating risk categories. That makes it difficult to drive any significant change in conjunction with procurement.
“Within accounts payable (AP), friction in settlements also creates a headache for treasury, especially when it comes to cross-border work and following up on payment deliverables. For complex industries like Transport & Logistics, the lack of digitization leads to inefficient processes in reconciling the cost.
“On a positive note, treasury teams are beginning to wake up to the challenges at play. Specifically, they are seeing the value and importance of real-time visibility across their business. There is still a long way to go and many of these teams still remain siloed, but treasury departments are beginning to move away from more traditional models and are becoming more open to solutions that have a broader business impact.”
“Digitization of AP is really the key first step to bringing value to sellers, treasury and AP. It provides a foundation of efficiency, delivering on earlier payments and the digitization of all information that is funneled into the settlement decision. This leads to lower barriers of entry for cross-department work, offering the opportunity to integrate with financial and insurance partners more seamlessly.”
“From the data side of things, most enterprises are faced with two main challenges. The first being that 80% of tier 1 supply chain data is poor quality. M&A activity causes specific challenges because unless that process of integration is carefully managed you end up with fragmented silo system landscapes. Then there’s the fact that, beyond tier 1 enterprises, most companies have very little insight into their data.
“What supply chain networks contribute is the ability to be able to put the supply chain participants at the center, which enables them to connect easier with other customers and their own suppliers. This puts it in their best interest to manage their master data, and in a network setting, every buyer and the embedded functions, whether that be treasury, procurement, AP or supply chain, can then use this to source information directly from the suppliers.
“The checks that are done across buyers, including validations, upload of certificates, filling in of information within regulated industries, has become an asset to the supplier that differentiates them in the marketplace and towards financiers. This allows the benefit to go straight back to the buyer in the form of clear, high-quality master data.
“Additionally, in a digital network play, both supplier onboarding and lifecycle management becomes digital, this includes things like contracts and agreements. For example, in supply chain financing solutions, this means that the network ensures that bilateral agreements are digital and visible to both parties and that users can have direct access to the actual trade and transactions. This creates a new level of transparency that companies cannot achieve with just digitization, having a network is vital within this equation.
“Poor information on the supply chain is a limiting factor that creates huge friction in many processes and/or limits the ability to get the most from your supply chain. Early payment is the simplest example of this; if you have poor information about who it is you are paying then it is far harder to provide early payments to them, the same principle applies when you are required to open a new bank account. To solve for issue is typically very time consuming and labor intensive, and is usually managed within a silo, whether that be poor ability to share or access the information of other activities.
“The lack of global standards means that it’s difficult to always know what is required and this can drive inefficiencies. An improved data set and source of transparency can help to improve operational efficiency within a treasury team, making it easier to deliver against regulatory requirements and business objectives.”
“When looking at the companies that reap the biggest revenues from their ML/AI investments, it’s always those that use emerging technologies to adapt to user behavior in real time. Whether it’s Google serving the right ads to the right people at the right time or Netflix informing your decision on what to watch next. We are only just starting to see an emergence of software that is aimed at helping employees in real time, whether that is informing spending decisions or prompting insightful user data.
“Another area where ML/AI excels is in analyzing diverse sources of structured and unstructured data, and deriving predictions and insights. That doesn’t mean that the technology is the key differentiator. Google and other tech giants are already openly sharing their technology, so it’s a commodity that brings everyone at the same level, but it’s in the way that you use it and how you source the data.
“In regard to the B2B space, there are two key components to look for. First is that the sweet spot for AI/ML is where huge data sets are available. Since the technology learns by example through wrong and right outcomes, you can foster the strongest AI by looking outside your own company and training the data from a whole industry or region perspective – you can’t derive value from applying AI within a silo. In a digital network or SaaS setting, you have the opportunity to leverage the best from a collective intelligence.
“Second, AI will be a key differentiator of driving efficiencies if you build tools on top of it that empower your employees. If it can leverage collective evidence across the company to make better spending decisions in real time against complex organizational objectives, or provide early warnings early in the process, even during for example vendor selection (pre-KYC), it would create more value to ensure that we don’t waste resources on factors that are more likely to fail on KYC later.”
Gert Sylvest is co-Founder of Tradeshift and GM of Tradeshift Frontiers.