How GE HealthCare Built an FX Treasury Program in Under a Year
When GE HealthCare spun off from General Electric, its treasury team had under a year to build an entirely new FX risk management program from scratch, including a $16B hedge portfolio across 160 countries.
This case study covers how the team partnered with AtlasFX to redesign its hedging architecture, cut annual trade notional by $8–10B and layered in AI-powered forecasting, work later recognized with the 2024 Silver and Gold Alexander Hamilton Awards for Financial Risk Management and Treasury Transformation.
- $8–10B annual trade notional reduction
- $16B hedge portfolio at spinoff
- 160 countries of operation
- ~120 hrs annual forecast time savings
A treasury built in under a year
When General Electric announced in 2021 that it would be separating into three independent businesses — GE HealthCare, GE Aerospace and GE Vernova — the treasury implications were enormous. For decades, core treasury activities had been centralized within GE corporate. Each spinoff entity would need to build its own function from the ground up, optimized for its own business model, regulatory environment and financial profile.
GE HealthCare (GEHC) manufactures MRI and CT scan machines, ultrasound equipment and contrast media, and sells to hospitals, clinics, universities and research facilities in 160 countries.
The company’s FX exposure was substantial from day one: a $16 billion hedge portfolio derived from two primary types of risk: long-term profit-and-loss (P&L) exposure hedged over a three-month to six-quarter horizon, and balance sheet remeasurement risk requiring an approximately monthly hedging cycle across nearly every country of operation.
Andrew Walecka, treasury operations manager with responsibility for FX risk management, was at the center of the build-out. The challenge was not simply to replicate what GE had done; the team had less than a year, a leaner headcount and no ability to carry forward GE’s legacy of custom, homegrown tools. They needed to design a better program than what had existed before, and implement it at speed.
Why was GE HealthCare’s legacy FX hedging approach a problem?
The legacy approach to FX hedging at GEHC was straightforward in concept but inefficient in practice. Each legal entity hedged its own exposure directly with the market, one-to-one. A dollar entity with euro risk went to Wall Street with that exposure. A euro entity with dollar risk did the same. At the consolidated level, many of these positions offset each other, but GEHC was paying bid-ask spread on both sides and managing a grossed-up book of derivatives far larger than its true net exposure.
“We had a very grossed-up set of derivatives, which we needed to resolve as we stood up our new treasury function.”
— Andrew Walecka, Treasury Operations Manager, GE HealthCare
Reducing derivative notional was an explicit priority from the treasurer. The goal was to shift to a many-to-one architecture: aggregate exposures across the legal entity matrix, place external hedges only for the true net risk, and carve those hedges back internally so each entity received its own internal hedge.
In theory, it was a cleaner and more efficient structure. In practice, implementing it required an end-to-end overhaul of resources, processes and technology.
The technology challenge was compounded by GE’s departure. GEHC could not take GE’s custom exposure management tools, built up over decades, and would not have the IT infrastructure to support them anyway. The team had to select, configure and deploy a new platform on a compressed timeline, with the precision that a regulated healthcare company’s legal entity structure demands.
How did GE HealthCare choose an FX risk management platform?
The selection process was deliberate. The treasury team ran workshops to define detailed requirements, then evaluated vendors via structured demos scored against a predefined rubric.
The core requirement was depth: the ability to model GEHC’s legal entity matrix in full fidelity, forecast at the individual flow level, aggregate to the consolidated level, and support the internal hedge carve-out process, all within a compliant, audit-ready framework.
GEHC selected AtlasFX and engaged PwC as implementation partner. The healthcare business operates through distinct legal entities in nearly every country (a consequence of the regulatory environment), creating what Walecka describes as a large web of group companies with FX risk falling out at varying points. AtlasFX’s architecture, built specifically for FX risk management by practitioners with corporate treasury experience, proved capable of handling that complexity.
How did GE HealthCare implement and test the new system?
Building out the legal entity matrix in AtlasFX was the most demanding aspect of the deployment. GEHC had to define internal settlement timing across all its entity relationships, a detail that matters because the timing of internal hedges determines the rates used for those transactions. With 250+ legal entities, many-to-one aggregation requirements, and specific settlement actions required during finite time windows, the customization effort was substantial.
The team approached testing with discipline. For a period, they ran what Walecka calls penny tests: small trades designed to probe every conceivable scenario. Early closeout of a trade. Changing amounts mid-stream. Shifting maturity dates. Every trade type, every trade action, every legal entity jurisdiction. Even a well-designed system will miss edge cases, and the team was determined to surface them before going live.
“I vastly underestimated how much work this project would be. Fortunately, our treasurer understood the complexity of what we were planning to do — better than some of us who were down in the weeds.”
— Andrew Walecka
The collaboration model mattered as much as the technology. Under GE, treasury, trading and accounting had operated in silos, each function receiving outputs from the prior one and passing their work on in turn. As an independent company, the entire team sat together. Working through decisions in the same room and understanding what was important to each function made a tangible difference to the quality of the outcome. Walecka credits cross-functional alignment as one of the key factors in the project’s success.
How does GE HealthCare manage balance sheet and cash flow hedging in real time?
Today, AtlasFX sits at the center of how GEHC manages FX risk day to day, from balance sheet remeasurement to cash flow hedging. The platform ingests financial data from both of GEHC’s enterprise resource planning (ERP) systems, including historical data, and pulls in outstanding derivatives from the accounting system. The treasury team adds forecasted transactions, plugging the macro-level FP&A forecast into the legal entity matrix before loading it into AtlasFX.
Hedge accounting requires specificity. Exposures cannot be hedged at the top of the house; they must be attributed to the legal entities that carry the risk.
From that input, AtlasFX calculates each entity’s net exposure and produces a hedging recommendation. For balance sheet hedges, GEHC generally executes in the suggested amount. For P&L hedges, the process is more deliberate: the team takes a layered approach, hedging a high percentage of current-quarter exposure and lower percentages in subsequent quarters. Recommendations are discussed in the context of market conditions, forward carry and forecast confidence before specific hedge targets are set for each upcoming quarter.
Approved trades are transferred from AtlasFX to 360T for execution by GEHC’s trading team in Dublin. The workflow is end-to-end: exposure aggregation, forecast, hedge recommendation, approval and execution all flow through the platform. Custom reports translate all exposures into USD on a consolidated, netted basis. That eliminates the madness, as Walecka puts it, of hedging offsetting currency-pair positions against each other, giving the team a clear view of true net P&L and balance sheet exposure at any time. The company’s tax team is also embedded in the process, reviewing internal transaction rates to ensure they meet transfer pricing standards across jurisdictions.
How accurate is GE HealthCare’s AI forecasting for P&L and balance sheet exposures?
With the foundational FX risk management infrastructure in place, GEHC’s treasury team turned its attention to a persistent challenge: the structural gap between how FP&A forecasts FX and how Treasury needs to act on it. FP&A works at the currency and macro level. Treasury must place hedges at the legal entity and flow level. Manually translating one into the other across 300+ individual flows, each with its own dynamics and accuracy profile, was time-consuming and produced results that varied by flow and by quarter.
Working with AtlasFX, GEHC deployed the platform’s AI forecasting module to automate and improve this process. The engine uses machine learning to analyze 48 months of standardized historical data per flow, incorporating GEHC-specific variables including seasonality and business correlations, and generates forecasts 18 months forward across all active flows. Each month, as new actuals post to the ledger, the model updates automatically.
Before adopting AI as the go-forward methodology, the team validated it rigorously. AtlasFX AI re-cast forecasts over an 18-month period and compared AI accuracy against manual forecasts on a mean average error basis across 253 flows in 2024 and 383 flows in 2025, the increase reflecting a ledger restructuring.
The results were compelling: approximately 80% of P&L flows improved, and 60% of balance sheet flows improved, with accuracy gains that continued to build as the model accumulated more data.
“AI FCST [forecast] much closer to actuals than Baseline Treasury FCST. Increased safety and confidence on CF [cash flow] Hedging levels.”
— Andrew Walecka
What results has GE HealthCare achieved with AtlasFX?
The results of the overall program are significant and span multiple dimensions. The shift to many-to-one hedging has reduced GEHC’s FX trade notional by $8 to $10 billion per year, saving directly on bid-ask spread and reducing the operational effort required to manage the book. Consolidating trade settlement with the middle office has generated further efficiencies that translate into real dollar savings.
Key results at a glance:
- $8–10B annual reduction in FX trade notional
- $16B hedge portfolio actively managed across 160 countries
- ~120 hrs of annual forecasting time saved via AI
- 80% of P&L flows showed improved forecast accuracy with AI
The visibility improvements may be equally important. The treasury team can now isolate anomalies faster, understand the drivers of balance sheet remeasurement in real time, and respond to surprises as they surface during month-end close, rather than simply reporting the number. The freed-up capacity from the AI forecasting improvement, estimated at approximately 120 hours annually, is redirected toward analysis and strategic engagement with FP&A, investor relations and business partners.
“The accuracy, speed and confidence in the exposure data we are now generating are infinitely better than with our manual, Excel-based approach. We are in a much better place, and we continue to improve.”
— Andrew Walecka
In November 2024, Treasury & Risk recognized GEHC’s program with the Silver Alexander Hamilton Award in Financial Risk Management, one of the most prestigious awards in corporate treasury, citing the sophistication of the many-to-one hedging architecture and the technology-enabled approach to managing one of the broadest FX programs of any global healthcare company. GEHC also received the 2024 Gold Alexander Hamilton Award in Treasury Transformation, recognizing the full scope of the build-out that followed the spinoff. Together, the two awards reflect a team that did not simply rebuild what GE had; they built something better.
