Theo Wasserberg, Head of UK&I at Embat, shares how AI and real-time data are transforming treasury management and shaping the future of finance.
Theo, what pivotal moments shaped your journey into fintech and treasury management, and what continues to inspire you in this space?
Earlier in my career, I worked with SAP and major implementation partners, delivering finance systems for large enterprises. During that time, I did a piece of work looking at the cost and time required to implement traditional treasury systems. It often took about 15 months and substantial investment to build the required capabilities. Embat can deliver these in a fraction of that time and cost.
I realised that mid-market firms were particularly underserved – too large for basic tools, but not large enough for the expensive, complex legacy systems. That gap in the market and the opportunity to genuinely revolutionise treasury management continue to inspire me.
During my MBA, I was a founding member of an AI club, where students and businesses would come together to discuss AI’s practical applications across different industries. That experience shaped my conviction that AI is fundamentally a problem-solver, not just a technology trend.
Treasury systems are particularly ripe for AI transformation. Today, leading an AI-native business at a time of critical industry transformation feels like the natural culmination of that journey.
The first quarter of this century redefined what was possible – driven by the democratisation of access, the rise of automation, and the relentless removal of friction. The next quarter will be shaped by something deeper: data that learns, intelligence that anticipates, and systems that think alongside us. The question isn’t if you need to modernise, it’s how fast.
How are current macroeconomic pressures—like rising interest rates and inflation—reshaping the treasury needs of medium and large businesses in the UK?
The macroeconomic environment has elevated the role of the treasurer. Rising interest rates, currency fluctuations, and inflation mean that cash management has become critical. Modern treasurers and CFOs are at the centre of the strategy and decision-making process.
Delayed data is a risk. Businesses can no longer rely on old systems that only give a snapshot of data once a week and spend hours, if not days, sifting through Excel documents.
Embat is helping to revolutionise the move to real-time visibility, management and intelligence. The companies that thrive are those using dynamic data to make faster, more informed decisions about liquidity, hedging, and investment
In what ways is Embat’s platform revolutionising how companies centralise cash, accounting, and payments? How do you see this evolving in the next 3-5 years?
Traditionally, treasury teams have been drowning in spreadsheets and week-old data, downloading bank statements weekly and reconciling transactions by hand. For a junior team member, that could account for a large portion of their working week.
Embat changes that by connecting data in real-time and using AI to automate repetitive work. Instead of looking backwards, finance teams can now see their positions instantly and make strategic decisions proactively.
Over the next few years, this will evolve even further with deeper ERP integrations, near-instant reconciliations, and predictive forecasting that make finance operations almost fully automated.
Tell us about TellMe, Embat’s AI-powered treasury analyst. How is AI transforming treasury operations and finance teams’ workflows?
Traditional reconciliation systems break the moment things get complex. TellMe is transformative. It’s already in use with our customers. It sits at the core of Embat’s platform and layers AI onto real-time data. That combination moves teams from simply recording what’s happened to understanding what’s happening now, and predicting what comes next.
For example, in bank reconciliation, traditional systems depend on rigid rules and break easily when faced with non-standard cases. TellMe’s AI recognises patterns, tolerates small variances, and automatically matches payments, even when they don’t fit a standard template. It also forecasts cash flows, identifying trends and seasonality, so teams can plan more effectively.
How do emerging regulations like PSD3 and open banking maturity influence treasury operations, and why is real-time cash visibility more crucial than ever?
As PSD3 reshapes data-sharing rules, open banking reaches maturity, and real-time payments and intelligence become the norm, the gaps exposed in that Thursday-afternoon scramble – delays, blind spots, and manual workarounds – will only widen.
The shift from compliance to capability is accelerating. Instant payments are now table stakes; what differentiates leading finance teams is how clearly and automatically they understand their cash position in real time. PSD3 enhances API standards, improves fraud data-sharing, and strengthens liability frameworks, all of which drive a need for greater transparency and control.
Finance teams must now connect directly to their banks and ERPs in real-time, removing the lag between transaction and insight. Finance teams don’t just need to move money fast; they need to understand and steer their cash position in real-time, directly from their ERP. That means no delays in reconciliation, no black boxes, and no legacy drag.
With AI automating routine tasks, how do you envision the role of finance professionals changing in strategy and decision-making?
AI removes the time-consuming operational burden – tasks like matching transactions or compiling reports – so teams can concentrate on future cash flows, funding models, and scenario planning. This shift also allows finance to respond in real-time to changes in the market. In short, automation liberates people from process, so they can become true strategic partners to the business.
Scenario modelling, risk management, and horizon scanning are areas where the benefits will become more apparent with AI. I don’t have to spend hours or days doing manual work to try to model the effect. If I can tell the AI what’s happening, I can get an answer in almost real-time and no key business decisions are delayed.
That could mean shipping a product earlier to mitigate against taxes or higher shipping costs; or locking in a currency exchange at a more favourable rate in anticipation of an interest rate rise. It’s a painless experience that saves time but has the potential to substantially improve risk management and business operations.
If you’re a person starting in finance today, you’re not just inputting data and creating reports generated by Excel. You expect to have two-way conversations with your finance system. Agentic AI tools will want to make sure you want to do that and offer better alternatives where possible. This is the future of finance.
You’ve mentioned that even the original disruptors are now at risk of disruption. How should fintech companies adapt to stay ahead in this rapidly evolving landscape?
We saw a wave of innovation and automation after the 2008 financial crisis. Some of this was driven by regulation, and some of it by the need to simply improve existing processes – domestic and cross-border retail payments are great examples.
The key to avoiding being disrupted yourself is to stay deeply connected to what finance teams actually need. Automation alone is no longer a differentiator; AI has taken that to the next level.
Companies must put AI front and centre, close the gaps left by legacy systems, and rethink how they solve problems. The expectation now is near-total automation and intelligent systems that don’t just execute commands but proactively recommend better decisions.
How have partnerships, such as with MacroFin, Google Cloud’s Vertex AI and others accelerated Embat’s growth and ability to serve complex multi-entity treasury needs?
Our partnership model is central to how we scale. We work directly with ERP and finance systems providers, and with implementation specialists like MacroFin and RSM. These firms are moving toward offering holistic solutions for finance departments, and Embat fills a crucial gap on the cash management side.
Our collaborations extend across all ERP ecosystems from Oracle NetSuite to Microsoft Dynamics, where we bring a layer of treasury expertise to the finance transformation project. By integrating with partners who are already driving digital transformation, we help their clients modernise treasury processes end-to-end, particularly in multi-bank, multi-entity environments.
Could you share examples of how Embat addresses unique treasury challenges across sectors like retail, logistics, and SaaS?
Every sector thinks its treasury problem is unique, and they’re half right. While the challenges vary, the common need is precision and real-time clarity.
In retail, managing cash flow volatility can make or break a business. Margins are being squeezed amidst rising profit warnings, consumer spending pressures and higher operational costs. Real-time cash visibility and intelligence are critical during seasonal peaks and enable more precise inventory investment decisions.
The logistics sector also faces mounting payment pressures, with nearly half of road transport invoices settled after their due date. This creates an urgent need for real-time supplier management, customer payment tracking and operational cash flow.
SaaS businesses face unique challenges with subscription billing models, where up to 35% of recurring payments can fail. Real-time tracking of actual collected revenue versus booked revenue provides accurate recurring revenue insights, while accurate, real-time financial data improves investor confidence and enables more precise growth forecasting.
What guiding principles have helped you lead finance teams through uncertainty, and what advice would you give CFOs and fintech entrepreneurs modernising financial operations for the next decade?
First, stay close to your users, understand their pain points and what truly matters to them.
Second, embrace AI and automation as strategic enablers, not threats. Finance teams want to think ahead, not wrestle with yesterday’s data. The leaders who’ll succeed are those who equip their teams with tools that automate the manual and elevate the human.
The era of batch data and disconnected systems is over. PSD3, open banking, and real-time payments are redefining expectations for visibility and speed. My advice: modernise now, not later – don’t enter another financial year with 2020s tools in a 2030s economy.
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