While SAP S/4HANA provides a strong ERP foundation, standard functionality alone often cannot address complex, organization-specific requirements. Our suite of tools helps organizations automate processes, integrate systems, and optimize finance and treasury operations. From mass uploading business partner data to automating transfer pricing, managing payment cut-offs, and sending accurate exposure data to Kantox through the SAP Kantox Adapter Tool, these solutions save time, reduce errors, and deliver measurable business value—all while fitting seamlessly into existing SAP landscapes. 

What many don’t realize is that the SAP S/4HANA Cloud, public edition, can also be tailored to specific business needs. Through extensibility, your ERP system can be enhanced without touching the core code, ensuring full compatibility with SAP’s updates. By enabling organizations to add tailored business logic, extensibility supports innovation, process optimization, and capabilities that go beyond standard cloud functionality. 

This article will first explore the bespoke solutions we offer and then shift to the extensibility options available in SAP’s public cloud edition. 

SAP Business Partner Upload Tool  

The Business Partner (BP) Upload Tool accelerates the creation and maintenance of SAP Business Partner records—traditionally a long, manual, and time-consuming task—by enabling mass uploads of key data such as names, roles, IDs, addresses, settlement instructions, and other master data attributes. 

Zanders’ BP Upload Tool streamlines these tasks using a structured Excel template with multiple worksheets, allowing users to choose the level of detail they want to maintain for each Business Partner. 

Key features and benefits of the BP Upload Tool: 

• Upload unlimited data records 

• Process hundreds of BPs within minutes 

• Handle thousands of settlement instruction records 

• Track and trace upload results through a detailed system log 

SAP Integration with Zanders Transfer Pricing Solution (TPS)

In addition to master data management, organizations also face efficiency challenges in other treasury-related areas. One of the most critical is managing intercompany transactions and ensuring accurate, compliant transfer pricing. This is where Zanders’ Transfer Pricing Solution provides additional value. 

Zanders’ cloud-based Transfer Pricing Solution automates the pricing of intercompany deposits and withdrawals, reduces manual effort in treasury workflows, and generates detailed audit-ready documentation for every pricing run. 

To support this process, we have developed an integration tool that leverages web services and APIs to automatically price cash pool transactions directly from SAP S/4HANA in an OECD-compliant manner. 

This integration component is estimated to save our customers around 100 hours of manual work annually, on top of the benefits derived from lowering the risk inherent in transfer pricing calculations. 

Zanders SAP Kyriba Interface 

Effective treasury operations rely not only on accurate pricing but also on smooth integration between ERP and treasury platforms. To address this, Zanders has developed tools that enhance these processes while minimizing manual effort and operational risk. 

Zanders SAP Kyriba Interface is an integration framework that automates payments, accounting, cash forecasting, and internal settlements between SAP and Kyriba. It streamlines data flows end-to-end and reduces the need for manual intervention. 

The interface manages key processes across multiple areas: 

• Outbound Payments 

Enhances SAP’s Data Medium Exchange Engine (DMEE) to generate payment files according to the Kyriba file naming convention. This ensures straight-through processing and control over file routing and payment execution in Kyriba. 

• Inbound Accounting  

Imports accounting data from Kyriba into SAP via BAPI (Business Application Programming Interface), with validation and duplicate prevention for cash and financial transactions. 

• Outbound Cash Forecast 

Exports open AP/AR balances and cash flow data from SAP to Kyriba for forecasting and bank mapping. 

• Outbound Internal Settlement 

Exports SAP AP/AR items eligible for internal settlement to streamline intercompany reconciliation and settlement processes. 

SAP Kantox Adapter Tool 

Beyond cash management and payment automation, organizations also face challenges in handling currency exposures. Zanders provides a dedicated solution to enhance this process and ensure accurate, consistent data for Kantox. 

The SAP Kantox Adapter Tool is a streamlined interface that extracts currency exposure data from SAP sales and purchase orders and sends it to Kantox through a secure API. It standardizes data mapping, automates processing, and provides an intuitive SAP interface for reviewing and transmitting exposures, including updates and cancellations. 

Key Features 

• Automated extraction and mapping of SAP order data into Kantox-ready JSON 

• Secure API communication with token-based authentication 

• ALV screen for filtering, reviewing, selecting, and processing exposures 

• Handles new, updated, and cancelled items with delta logic 

• Customizable configuration for API parameters and filtering rules 

Key Benefits 

• Reduces manual processing and operational risk 

• Ensures accurate, consistent exposure data for Kantox 

• Improves transparency through real-time status updates and logs 

• Easily adjustable to business requirements via configuration 

Automated Cut-Off Time Handling in SAP Payments 

In addition to integrations, payment timing is another area where automation can significantly reduce risk. Zanders’ automated cut-off time enhancement addresses this challenge. 

This SAP enhancement automates the handling of internal payment cut-off times within SAP In-House Cash. Banks enforce daily cut-off times—deadlines after which payments are processed on the next business day. Standard SAP logic does not always account for these constraints, which can lead to delays or missed payments. 

 How the solution works: 

• Automatically checks if a payment is being made too late in the day 

• Adjusts the payment date to ensure it meets the bank’s cut-off time 

• Uses SAP’s Business Rules Framework (BRF) to tailor logic to company-specific needs (e.g., currency, bank location, urgency) 

Key Benefits: 

• Fewer payment delays 

• Improved cash flow planning 

• Eliminates manual checks 

• Seamless fit with existing SAP systems 

• Customizable to business rules 

These tools are compatible with SAP S/4HANA On-Premises and Private Cloud editions and are deployed under Zanders’ namespace. This ensures they do not interfere with the customer’s own developments and allows Zanders to safely deliver support updates without impacting the customer environment. 

While Zanders’ tools streamline SAP processes like payments, currency exposures, and transfer pricing, some clients on SAP S/4HANA Public Cloud may think there’s no room for customization. In the next section, we will explore the platform’s extensibility capabilities, which allow organizations to adapt and extend their cloud system safely, without altering the core code. 

Tailor Your Cloud: How Extensibility Empowers SAP S/4HANA Public Edition 

SAP S/4HANA Cloud, public edition, is a standardized, continuously updated cloud ERP system. For many organizations, this standardization provides a solid foundation for managing core business processes like finance, procurement, and logistics. However, every business has unique competitive differentiators or regulatory requirements that the standard software simply cannot address. 

As adoption of SAP’s cloud solutions grows, organizations increasingly want these platforms to meet their specific business needs. 

This is where Extensibility comes in. 

Extensibility is the built-in capability that allows users and developers to adapt, extend, and integrate new functionality into S/4HANA Cloud without modifying the core code. This "Clean Core" approach is essential in a public cloud environment, ensuring that custom solutions remain fully compatible with SAP’s quarterly updates. Think of it as installing modern amenities in a historic building—you can add new features without altering the original structure. 

The Three Pillars of Cloud Extensibility 

The flexibility to build custom solutions on S/4HANA Cloud is delivered through a spectrum of tools designed for different needs and skill sets. These capabilities range from simple key-user adjustments to full developer-driven extensions, with the level of technical proficiency increasing at each stage: 

1. In-App Extensibility (Key-User Tools) 

This capability empowers business users and functional analysts—Key Users—to make immediate, direct changes within S/4HANA Cloud. These are typically simple, administrative-level modifications that improve day-to-day usability. 

• What you can do: Add custom fields to master data records (e.g., a new compliance code for a vendor), rearrange screen layouts, or create custom business logic rules such as a simple approval workflow. 

• The Benefit: Agility and self-service. Changes can be implemented quickly by the people who best understand the business need, without traditional programming. 

2. Developer Extensibility 

For more complex business processes that must be tightly integrated with the S/4HANA core, professional developers use the Embedded Steampunk environment. This allows them to write sophisticated custom code directly within the S/4HANA system while adhering strictly to cloud-compliant APIs (Application Programming Interfaces). 

• What you can do: Build custom applications or highly specialized business logic (like a unique pricing algorithm) that needs access to core data in real-time. 

• The Benefit: Deep integration and performance. Custom code is co-located with the ERP system, ensuring high-speed data access and seamless operation with core processes. 

3. Side-by-Side Extensibility (SAP Business Technology Platform - SAP BTP) 

The most innovative and decoupled solutions are created using the SAP Business Technology Platform (SAP BTP). This platform acts as an innovation layer, running custom applications that securely consume and extend S/4HANA Cloud services without being inside the ERP system. 

• What you can do: Create entirely new user experiences (e.g., a custom mobile app for field service), build complex data insights, integrate with external cloud services (e.g., a logistics provider), or develop automated workflows using low-code/no-code tools such as SAP Build. 

• The Benefit: Innovation and decoupling. Because the custom solution is separate, it can be developed, updated, and scaled independently, offering the highest level of stability for the core ERP and a path to unique digital capabilities. 

As organizations evolve their finance and treasury functions, the need for systems that are both efficient and adaptable has never been greater. Zanders’ SAP solutions—ranging from automating master data and transfer pricing to integrating treasury platforms and streamlining payments—demonstrate how targeted enhancements can unlock significant operational value. 

At the same time, the extensibility capabilities of SAP S/4HANA Cloud show that innovation can coexist with standardization, allowing companies to tailor their ERP without disrupting core processes. 

By combining deep SAP expertise with a clean-core approach, we enable organizations to transform today’s operations while building a flexible, future-proof foundation. This empowers companies to improve efficiency, strengthen compliance, and extract maximum value from their SAP environment. 

Contact us to assess fit, request a demo, or learn more about our add-ons and technical capabilities in the public cloud.

Fragmented systems, manual reconciliations, and delayed reporting make it difficult for finance teams to see the full picture of their liquidity. As a result, valuable cash remains tied up in receivables, payables, or inventory — limiting flexibility and slowing growth.

Recording and tracking working capital solutions within the ERP system ensures transparency, auditability, and control over liquidity. Clear records of financing activities enable better cash flow monitoring, compliance, and financial reporting, while supporting informed decisions and stakeholder trust.

With SAP S/4HANA, organizations can turn visibility into action. By bringing financial, operational, and supply chain data onto a single digital platform, S/4HANA delivers real-time insight into how cash moves through the business. Finance and treasury teams gain the clarity to forecast more accurately, act faster, and unlock liquidity that was once tied up in day-to-day operations. The result is  stronger cash flow, lower funding costs, and a more agile, financially resilient organization.

How ECC handled working capital management processes

In ECC, recording working capital management functions relied on limited solutions such as the “Pledging Indicator” in Accounts Receivable, which offered tracking but little automation. This often led to manual workarounds and reduced transparency. The Contract Accounts Receivable and Payable (FI-CA) module offered a factoring solution to sell approved receivables to a factor bank with full accounting integration and automated postings for transfers, payments, and settlements. However, its use was largely restricted to industries such as insurance with high-volume customer billing. SAP S/4HANA changes that by embedding working capital processes directly into the digital core — enabling real-time liquidity visibility, automated settlements, and integrated accounting for financing activities.

Solution options in S/4HANA

Optimizing Working Capital with SAP S/4HANA empowers organizations to strengthen liquidity, reduce funding costs, and enhance operational agility through an integrated digital core. Built on the SAP HANA in-memory platform, it delivers real-time visibility, predictive insights, and automation across financial and operational processes that directly influence working capital performance.

S/4HANA Settlements Management is one such solution that brings together a wide range of financial and commercial settlement processes to streamline how organizations manage payables, receivables, rebates, commissions, and cost allocations. Its functionalities—covering financial settlements, rebates, commissions, freight and cost allocations, factoring, intercompany charges, and chargebacks—come together in one unified system that reduces scattered processes and cuts down manual work across departments. Integration with Accounts Receivable, Accounts Payable, and Treasury enables real-time insight into financial exposure, helping finance teams make informed decisions on funding and liquidity. By consolidating transactions, automating complex calculations, and enabling transparent, rules-based settlements, the solution accelerates cash cycles, improves forecasting accuracy, and enhances liquidity. Among its many capabilities, the factoring functionality is significantly enhanced in S/4HANA compared to ECC, offering a far more integrated and automated approach to managing receivables financing.

Factoring in SAP S/4HANA Settlements Management: An integrated approach

S/4HANA provides an enhanced factoring solution within Settlements Management (for private cloud), that allows organizations to manage factoring agreements with banks in a structured, automated, and auditable way. It brings transparency to the end-to-end process while improving liquidity on short notice. Below steps highlight the overall process under Factoring solution:

SAP’s future innovation strategy for working capital management focuses on delivering integrated solutions like SAP Taulia Working Capital Management which is an embedded solution within SAP’s Treasury and Working Capital portfolio that helps buyers and suppliers optimize cash flow across payables, receivables, and inventory. Built into SAP ERP and Business Network, it enables seamless integration, real-time insights, and scalable liquidity management to unlock cash, reduce costs, and strengthen supplier relationships. Taulia-based factoring capabilities are offered since 2508 for S/4HANA Private and Public Cloud customers.

Building on these operational capabilities, SAP also enables organizations to gain deeper analytical visibility across the entire working capital cycle. To support this, SAP offers Working Capital Insights — a pre-built intelligent application in SAP Business Data Cloud that consolidates data from receivables, payables, inventory, and cash to deliver unified working-capital KPIs, trends, and forecasts. It gives finance teams rapid visibility, liquidity insights, and actionable optimization opportunities with minimal setup.

Choosing the right Working Capital Solution: Drivers for selecting the appropriate option

When comparing different solution options for working capital optimization, the key difference lies in managing financing relationships and the automation that is provided out of the box. Settlements Management provides the customer with a toolset to configure the handling of predefined finance providers, making it suitable for organizations with stable funding arrangements and long-term partners. In contrast, Taulia offers a fully embedded digital marketplace that connects businesses with multiple funders through one platform, enabling flexible, transparent, and competitive financing. This makes it ideal for organizations seeking dynamic or global funding options. For any new S/4HANA Private or Public Cloud customer, selected entitlements from Taulia are included as part of the ERP entitlements at no additional subscription cost. These cover key capabilities such as Factoring, Virtual Cards and Dynamic Discounting. However, organizations that need broader or more advanced Taulia capabilities can unlock the full feature set by opting for the premium version.

Beyond cost and system integration, the choice between the two solutions should be guided by business drivers, such as:

Business Driver	Considerations
Stability of existing funding arrangements	Assess if current financing options are stable and already meet liquidity goals.
Choice of funders and financing structures	Evaluate the need for flexibility and visibility in selecting funders or dynamic funding models.
Cash Conversion Cycle (CCC)	Review the organization’s CCC and determine if further optimization is required.
Agility to external pressures	Assess how quickly the organization needs to adapt to market or liquidity changes.
Supplier and buyer onboarding and adoption	Evaluate how easily suppliers and buyers can be onboarded to the chosen platform.
Implementation complexity and time	Compare expected implementation effort, timelines, and traceability features.
Scalability and geographic reach	Determine if the solution supports multi-country operations and varying business volumes.
Integration with existing SAP landscape	Assess how well the solution fits within the current SAP environment and data flows.

Ultimately, the decision should align with the organization’s strategic liquidity objectives, operational maturity, and supplier ecosystem, ensuring that the selected platform not only optimizes working capital but also enhances transparency and control.

From Insight to Action: Guiding Your Working Capital Strategy with expertise

Choosing the right working capital solution is not just a technical decision — it’s a strategic one. Turning insights into action requires time, focus, and a clear understanding of both business and treasury priorities. Many organizations find it challenging to balance day-to-day operations with the strategic effort needed to evaluate and implement the right solution.

We supporst organizations in navigating this complexity by combining deep treasury expertise with in-depth SAP knowledge. Our consultants take an objective and structured approach;  assessing business drivers, current processes, and operational requirements to identify the solution that best aligns with your strategic goals. Beyond technical evaluation, Zanders acts as a strategic partner, ensuring that your chosen solution supports broader liquidity, financing, and operational objectives.

Introduction

In the previous article, From Day 1 to Strategic Partner: Building a Treasury Function for a Carved-Out Business, we highlighted the importance of a tailored target operating model (TOM) to establish a solid strategic and organizational base for the new treasury. Following the definition of the treasury TOM and once day 1 readiness measures are in place, the next priority is to focus on value creation via cash and liquidity management, which represent another key pillar for a successful treasury implementation and treasury process. Cash and liquidity capabilities play a vital role in ensuring treasury delivers both operational continuity and strategic value. This article outlines how to build that foundation by establishing robust and forward-looking cash and liquidity management processes already at the time of the transaction, addressing immediate post-transaction needs, and enabling mid-term process efficiency.

Prepare for Day 1: Managing Liquidity Readiness

When preparing for the closing of an M&A transaction, treasury plays a critical role in ensuring that the new organization operates independently from Day 1. Liquidity planning is essential as cash pressures often peak around the time of legal close.

The following aspects may erase cash reserves if not properly anticipated:

• Upfront costs (transaction fees, legal, advisory, integration).
• Debt financing frameworks still under negotiation.
• Cash repatriation constraints or internal investment requirements to support the separation.

Hence, treasury must address these topics in cash and liquidity planning by:

• Modeling short-term needs under multiple scenarios based on validated assumptions on the business’ structure and liquidity needs.
• Ensuring cash visibility and centralization of cash, where possible to manage funding efficiently.
• Evaluating working capital buffers and the need for interim funding lines.

By addressing these topics before closing, the new entity enters day 1 with visibility on liquidity positions, funding plans, and confidence in its ability to operate independently.

Manage Day 1: Establishing Control, Visibility and Centralization

For a newly independent entity, cash visibility is often fragmented across systems and bank accounts, especially in the early stages after a carve-out. Yet gaining (real-time) transparency is fundamental to effective cash management and decision-making. The foundational elements to achieve at this stage are:

• Set up efficient connectivity with all banking partners.
• Deploy treasury technology (e.g., a TMS or interim solution) to aggregate bank balances and transactions centrally.
• Implement daily cash positioning processes across all relevant bank accounts.
• Define responsibilities and control mechanisms for cash operations, ensuring a clear RACI model.

Cash visibility improves control and reduces risk while enabling better liquidity allocation across the group via a centralized cash management process. The deployment of cash concentration structures, such as cash pooling, allows the unlocking of financial resources and benefits, such as fee reduction or interest optimization. Furthermore, centralized cash management data is a prerequisite for AI-driven cash applications and greater financial risk exposure definition¹, which can significantly reduce manual effort in distributing and managing cash across the group.

Early Stabilization: Forecasting and Short-Term Control

Once operational continuity is secured, the focus should move to stabilizing cash and liquidity processes.

Forecasting in a post-carve-out environment is challenging, yet essential. Missing historical data, unclear transaction volumes, and transitional arrangements (e.g., TSAs) often reduce forecast accuracy.

A suitable solution is the deployment of a layered forecasting approach, including:

• Short-term cash flow forecasts (typically 13-week rolling) to guide immediate liquidity needs.
• Medium-term liquidity planning, integrated with business planning (FP&A) and CAPEX cycles.
• Stress-testing and scenario analysis to evaluate performance under simulated business conditions.

In our experience, cash forecasting is an evolving process, which can be optimized and automated over-time through data integration (e.g. from ERP system) and predictive modeling. With data quality as the foundation, cash flow forecasting can begin by establishing  the most accurate starting point and committed forecasts under company control, such as opening balances and invoice payables. Once the high-certainty inputs are captured, additional cash flows such as committed accounts receivable and other material forecasts e.g. sales forecast or CAPEX forecast can be integrated.

Carved-out entities must consider the growing maturity and quality of their systems and respective data over the first 12 months after day 1.

Designing a Fit-for-Purpose Liquidity Structure

Once visibility and forecasting are addressed, the focus should immediately shift to structuring liquidity flows across the new organization. The objective is to ensure funding efficiency, mitigate cash drag and trapped cash, and enable flexibility, all within the constraints of the newly formed legal and operational setup.

Key design considerations include:

• Tailored cash pooling and automated cash concentration structures.
• Intercompany funding structures, including currency and transfer pricing alignment and documentation.
• Liquidity buffers tailored to business volatility and seasonality.
• Working capital optimization levers (e.g., payment terms, supplier financing).

Hybrid liquidity management structures, combining centralized oversight with localized autonomy for operational banking, often achieve the best balance. Zanders supports clients based on its wide experience in bank relationship strategy and liquidity optimization for disentanglements.

Optimizing Cash Flow Management towards long-term state

Throughout the transition and towards the steady-state operations, treasury must monitor and manage cash & liquidity against an evolving backdrop of business integration activities and one-off events.

These may include:

• Working capital shifts based on new supply chains or changes in customer behaviour.
• Integration costs linked to systems, people, and process harmonization.
• Divestitures or asset sales to fund operations or sharpen the business focus.
• Cash flow issues caused by system delays or supplier renegotiations.

To address this, Treasury should establish daily cash positioning routines utilizing state-of-the-art technologies and tools, escalation mechanisms, and strong collaboration with FP&A, procurement, and tax. Treasury shall also foster a “Cash First” mindset in the newly created organization. This ensures quick resolution of bottlenecks and reinforce cash flow discipline.

Strategic Liquidity Considerations for Long-Term Success

Cash and liquidity decisions taken during a carve-out will influence the new company’s financial flexibility as it takes quite some time and effort to implement changes in liquidity structures considerably at a later stage. Therefore, treasury should consider as early as possible the following aspects:

• Debt and credit rating impacts, especially if carve-out funding involves leverage.
• Treasury risk centralization (especially FX risk), to reduce cross-border inefficiencies and improve hedging performance.
• Tax and regulatory considerations, such as repatriation limitations, transfer pricing, and cash tax leakage.
• Strategic investment readiness, ensuring adequate liquidity for future M&A, CAPEX, or digital transformation.

The liquidity setup must be scalable, allowing the business to respond confidently to  rapid growth, market volatility, or external shocks with resilient measures.

Zanders’ clearly sees a need for treasury functions to evolve into applying strategic enterprise liquidity models providing an efficient framework to link various stakeholders around the office of the CFO, including treasury, FP&A, risk management, accounting and more. A group-wide approach ensures alignment, cooperation and can lead to faster and more informed decision-making processes.

A Roadmap to Liquidity Maturity

The path to liquidity excellence starts with day 1 readiness preparation and implementation but extends far beyond. Treasury should approach this evolution through a structured roadmap that includes:

• Standardization of forecasting processes and technology tools.
• Centralization of liquidity governance, structures, and banking relationships.
• Continuous optimization of working capital, pooling structures, and investment of surplus funds.
• Measurement and benchmarking of liquidity KPIs and stress performance.

We bring a proven methodology and deep experience in day 1 planning and execution, as well as in post-M&A treasury transformation. We help clients design and implement cash and liquidity frameworks that deliver control, flexibility, and strategic value.

In the next edition of this series, we look at implementing effective banking strategy and funding practices within the newly carved-out entity, including key areas of focus and potential challenges.

Yet in many organizations, FCP models still follow the same rigid model validation (MV) process used for credit risk models, a process designed for stability, not agility.

While this ensures consistency, it leaves little room for the frequent updates these models desperately need. With regulators pushing for both effectiveness and compliance, and criminals moving faster than ever, the traditional approach is under pressure. Could generative AI, one of today’s most disruptive technologies, be the key to bridging the gap between strict validation requirements and the need for agility? Could it help us strike a better balance between speed, control, and cost?

The Current State of Model Validation in FCP

Currently, FCP model validation largely follows the framework used for credit risk models, but it faces several critical limitations:

• Manual, time-consuming and resource-intensive: Highly skilled validators must carefully review documentation, code, data pipelines, and monitoring outputs. Currently, it can take two people 6-8 weeks to complete a single validation, leaving them with little capacity to handle additional validations in parallel. In the time it takes to complete a validation, new criminal patterns may already be emerging which require updates or the development of a new model. In addition, delays in feedback between the validation team and developers could further extend the overall timeline.
• Costly: Compliance already consume significant budgets, and under cost-cutting pressures, it becomes increasingly difficult to justify allocating large teams to a single validation. 
• Inflexible: Current validation approaches, built around credit risk frameworks, are primarily designed to safeguard capital adequacy. While effective for that purpose, they are not structured to keep up with the fast-moving and evolving patterns of financial crime.
• Capacity constraints: Given the size of most financial crime model portfolios and the frequency of validations required by internal policies, current resources are unlikely to meet long-term expectations. With model inventories expected to grow, this gap will only widen unless capacity is increased.

These challenges highlight a pressing reality: the current validation approach is no longer sustainable. Without significant improvements, organizations risk falling behind with both regulatory expectations and the fast-moving tactics of financial criminals. 

Where Can GenAI Make the Biggest Impact in Validation?

In most organizations, models are assessed based on risk, complexity, and probability of failure. This tiered approach helps determine where automation, such as GenAI, can be applied most effectively.:

• High-risk, complex models – Models that are mathematically sophisticated, critical to operations or compliance, and have a higher probability of failure require thorough human validation and expert judgment due to their complexity and potential impact. Therefore, they are not well-suited for GenAI-based validation.
• Low-risk, simpler models – Models that are less mathematically complex, more standardized, and have a lower probability of failure. These are ideal candidates for GenAI, which can handle repetitive validation tasks, review documentation, and generate draft reports.

With GenAI taking care of routine checks on simpler models, human experts can dedicate their time and judgment to the models that truly require deep expertise.

The Role of AI in Financial Crime Model Validation

In practice, how can AI be applied to support model validation? At its core, AI is best suited to take on the repetitive and manual aspects of the process, the tasks that consume valuable time but add little judgment-based value. Instead of replacing experts, AI acts as an efficiency enabler – a “second pair of eyes” that enhances consistency, speeds up routine checks, and leaves human validators free to focus on areas where their expertise is irreplaceable. 

Generative AI opens new possibilities for how validation might be approached, with AI driving the validation steps. Instead of starting from scratch, it could make it possible to ingest large volumes of model documentation and generate draft answers to validation questions which are informed by guidance documents, policies, and historical validation reports. It may also be possible to highlight areas that need further clarification, suggesting relevant follow-up questions for discussions between validators and model developers. Where responses are already sufficient, GenAI could enable the automatic closure of open points, keeping the process moving smoothly. Beyond Q&A, it creates the possibility of drafting validation findings based on prior patterns and even producing structured, section-by-section draft validation reports, giving validators a strong foundation to build on, rather than a blank page to start with. Final review and submission are always completed by the validator. 

This shift highlights a clear evolution in validation practices. Currently, validation is often characterized by long checklists, manual document reviews, and labor-intensive report writing. With AI support, validation can become faster, more consistent, and highly scalable, allowing humans to focus on important aspects such as judgment, oversight, and final decision-making. With routine and repetitive tasks automated and accelerated through AI, and waiting times between interactions significantly reduced, validators could manage multiple validations in parallel. For model developers, this also means less time spent waiting on feedback, and therefore model developers can drive the speed of validation by submitting evidence faster. In short, AI doesn’t diminish the role of the validator – it elevates it, ensuring their expertise is applied where it delivers the most value. AI will transition the validator from an executor to a supervising role. 

How would this Technically Work?

The goal is to create a simple, working version of the idea that shows how GenAI can support validators by automating repetitive steps while keeping humans in full control. It’s not about replacing expertise but about giving validators a smart assistant that can read complex documentation, provide the right information, and draft initial outputs they can refine.

At the center of this setup is an agentic framework built around two main parts: a Retrieval-Augmented Generation (RAG) system and a prompt creation engine. The RAG system helps the AI pull the most relevant content from internal guidance, policies, and historical validation reports. The prompt engine then turns that information into focused, context-aware prompts, so the AI can generate accurate, useful drafts. Everything runs securely in the organization’s existing cloud environment (for example, in Vertex AI on GCP) to make sure data stays protected and traceable.

The process could look like this:

• The model developer submits documentation, and the AI reviews it to identify relevant guidance and validation standards.
• It drafts initial responses to validation questions, giving the validator something concrete to start from instead of a blank page.
• If information is missing or unclear, the AI compiles structured follow-up questions that the validator can check and send back to the developer.
• When new evidence comes in, the AI reviews it, links it to the open items, and flags what can be closed or what still needs attention.
• Finally, it pulls everything together into a draft validation report with structured sections and proposed findings, ready for the validator to review and finalize.

In this target setup, the AI tool sits between the model developer and the validator. It manages the flow of documents and questions, keeps track of progress, and helps draft findings and reports. Validators remain in charge of every decision but can move through the process much faster and with more consistency. Developers, in turn, get clearer feedback and shorter waiting times.

The outcome is a smoother, more efficient collaboration where AI takes care of the manual groundwork, and humans focus on judgment and oversight, the parts that really matter.

Balancing Benefits and Risks

The potential of AI in model validation is not just theoretical; it comes with tangible benefits. First and foremost is efficiency: automation can significantly reduce the time spent on repetitive validation tasks, freeing experts to focus on higher-value activities. With a basic introduction of AI into the validation process, teams can achieve time savings of around 30%. When introducing a more advanced option, we believe an estimated time saving of up to 80% can be achieved. This naturally translates into cost savings, as the overall validation burden is lowered without compromising quality or increasing headcount. AI also promotes consistency and transparency, applying the same standards uniformly across models. Finally, it offers scalability as organizations can handle a larger portfolio of models without needing to increase headcount, a crucial advantage given current cost-cutting pressure.

As with any innovation, AI in model validation comes with significant risks that must be managed. Generative AI itself carries model risk, including bias, opacity, or “black box” behavior, which could undermine confidence if not carefully controlled. Additional concerns include autonomy risk, where AI might generate outputs without sufficient human guidance, leading to decisions that may be inappropriate or misaligned with validation standards; hallucination risk, where it produces information that seems plausible but is factually incorrect, which could mislead validators if not carefully checked; and incompleteness risk, where AI may overlook parts of a model or validation requirement, resulting in partial or insufficient coverage.

These risks can be managed by humans actively supervising AI and regularly reviewing its outputs. Mistakes are far less likely when experts double-check results, make sure nothing is missing, ensure all information provided is accurate, and stay in control of key decisions. Regulatory acceptance is also a consideration, as supervisors are likely to scrutinize the role of AI and require organizations to explain and justify its use. Finally, careful implementation prevents over-reliance on automation, ensuring human validators remain central to decisions where judgment is essential.

When implemented with care and proper oversight, AI can bring significant benefits to model validation. By combining AI’s capabilities with human judgment, organizations can work more efficiently, handle greater scale, and reduce costs, all while maintaining the trust and rigor that model validation requires.

Our FCP expertise:

Zanders brings a unique combination of expertise in both traditional and AI-driven model validation, helping to navigate the evolving landscape of financial crime model oversight. As a trusted advisor in risk, treasury and finance, Zanders combines deep regulatory knowledge with practical experience, ensuring that solutions are not only innovative but also fully compliant. More importantly, Zanders focuses on pragmatic, regulator-ready designs that bridge cutting-edge technology with compliance requirements. Zanders helps organizations work more efficiently while still meeting the high standards of rigor and trust that regulators expect.

Conclusion

In this FCP series, we have explored bias and fairness, explainability, and model and data drift. Each represents a vital aspect of building models that are not only powerful, but also responsible. Together, they remind us that the real challenge is not just creating models that work, but creating models that we can trust, understand, and sustain over time.

This is what makes AI-enabled model validation a natural next step. As models become more complex, risks evolve faster, and regulatory expectations increase. Organizations need human experts to focus on the areas where their judgment and oversight have the greatest impact, while AI handles the routine tasks.

As we conclude this series, the message is clear:

Organizations that embrace GenAI in their validation processes are not just improving efficiency, but they are shaping the future of model risk management.

Treasury organizations should periodically review their technology landscape and solutions. The right data foundations, system integrations and top-notch analytics can provide immense business value through better insights, cost savings, workflow automation, and productivity gains.

To obtain these benefits, it is necessary to re-assess how your Quantum setup fits your business requirements, evaluate the newest features introduced in quarterly Quantum releases, and define an implementation roadmap after conducting a cost-benefit analysis.

With AI becoming increasingly accessible and more widely integrated into enterprise applications, the opportunities are greater than ever.

Your pit stop with Zanders: Quantum Health Check

At Zanders, our Quantum Health Check is designed to give you a clear, fact-based view of how your treasury system is performing today and where there’s room to grow, considering how your company and treasury organization evolved over the years. Our goal is to ensure that you can drive Quantum like a proper F1 car, benefit from its newest features, and leverage the opportunities related to such technologies as APIs, oData, EWF, dashboards, data warehouses/lakes, BI tools or (Gen)AI.

How do we do it? Each health check is tailored to your specific scope – we listen closely to your priorities and then bring our ideas and suggestions to the table. We dive deep into your Quantum setup and the surrounding solutions – reviewing configuration, data flows, system integrations, reporting, controls, and daily processes – using proven checklists and targeted workshops and interviews.

In less than a month, at the end of the review, we present to you and discuss an easy-to-understand assessment report with a practical action plan and a tailored roadmap, highlighting quick wins and longer-term improvements. Our approach ensures you have the insights needed to stabilize operations, address risks, and unlock new value from Quantum, all while keeping the process smooth and efficient for your treasury team.

TreasuryIQ: AI as Your Pit Crew and Co-Driver

Taking your Quantum Health Check to another level, Zanders seamlessly integrates our proprietary AI platform called TreasuryIQ and our cutting-edge AI services to deliver tangible innovation and value. With an option of involving a dedicated AI engineer throughout the process, clients gain access to hands-on (Gen)AI knowledge, people with experience of implementing AI use cases, and workshops that transform conceptual AI into practical, treasury-specific applications.

Imagine taking your TMS to the next level like swapping out a standard engine for a turbocharged hybrid, engineered for tomorrow’s demands. Our “TreasuryIQ Assistant” acts as your instant expert co-pilot, accelerating user support and insight. Our agentic solutions such as “TreasuryIQ Tester” ensure your workflows are not just automated but intelligently optimized. And specialized solutions such as “Transfer Pricing Suite” show you concrete examples of how AI can fit into your treasury technology picture.

What truly sets Zanders apart is our commitment to co-innovation: after the Health Check, we partner with you to pilot and scale new treasury AI solutions, empowering your team to reimagine what’s possible. With Zanders and TreasuryIQ, your treasury technology isn’t just race-ready – it’s rewriting the rules of the track.

Until recently, most UAE corporate entities were not subject to corporate income tax. This changed with the Ministry of Finance’s announcement on 31 January 2022, introducing a federal corporate tax regime applicable to financial years starting on or after 1 June 2023. The new regime also formalised the UAE’s transfer pricing framework as part of its commitment to the OECD BEPS standards.

Federal Decree-Law No. 47 of 2022 sets out the corporate tax rules and embeds transfer pricing requirements under Articles 34–36 and 55. In addition to the Federal Decree-Law No. 47, the UAE’s Ministry of Finance and Federal Tax Authority also issued additional rules and guidance specific to transfer pricing:

• Ministerial Decision No. 97 of 2023 – Sets out the conditions for the preparation of Master Files and Local Files in line with OECD BEPS Action 13.

• UAE Transfer Pricing Guide – Detailed guidance on the practical impact and implementation of transfer pricing regulations. The guide was prepared in alignment with the OECD’s Transfer Pricing Guidelines.

The incorporation of these rules, together with the growing attention from tax authorities on the Transfer Pricing of Intra-Group Loans, has significantly increased the focus of tax and treasury teams on the importance of transfer pricing in the region.

Importance for treasury teams

Over two years on from the UAE’s adoption of a formal corporate income tax regime, the region has positioned itself as a potential financial hub for multinationals to set up their centralised group finance and treasury functions. The UAE’s economic reforms and growing alignment with international financial standards further strengthen its case as a pragmatic and effective financial hub.

Multinationals are increasingly looking towards centralised, in-house financing functions to more effectively navigate variables between markets such as regulatory requirements, currency exposures, and broader liquidity and cash flow targets.

As more multinationals set up financing hubs in the UAE, scrutiny from the Federal Tax Authority will increase accordingly, particularly with a legitimate transfer pricing regime now in place. This means that both treasury teams and tax departments need to price intra-group loans on an arm’s length basis.

Alignment with the OECD TP Guidelines

The UAE follows the OECD TP Guidelines Chapter X for the transfer pricing of intra-group financial transactions, with additional local guidance provided in section 7.1.3.2 of the UAE Transfer Pricing Guide. Intercompany loans must reflect arm’s length terms, including loan amount, maturity, repayment terms, and arm’s length interest rates.

In practice, a 4-step process should be followed to comply with these requirements:

Step 1: As a first step, the terms and conditions should be reviewed to ensure their commercial rationale and that they reflect the actual economic reality of the parties. In this sense, special consideration should be given to the loan amount and whether an independent party would extend such an amount to the borrower. To this end, the so-called Debt Capacity Analyses are performed.

Step 2: As a second step, a credit rating analysis should be performed. While the recommended approach is to follow a bottom-up approach, based on the standalone credit rating of the entity adjusted for group support, in some cases a more simplified top-down credit rating approach can also be considered acceptable.

Step 3: As a third step, the pricing analysis is performed, typically by application of the external CUP method, identifying comparable third-party transactions with similar characteristics. Of course, the necessary comparability adjustments should be performed to reflect the differences between the external comparables and the loan under analysis.

Step 4: Finally, the analysis should be documented in a Transfer Pricing Report, explaining in a transparent manner the analysis performed in the previous steps. It is important to have legal documentation in place, reflecting the terms and conditions of the loan that have been considered during the analysis.

Interest limitation rules:

Alongside traditional transfer pricing regulations, the UAE also enacted interest deductibility rules in Article 30 of the Ministerial Decision No. 120 of 2023. The provisions are broadly modelled after the OECD’s BEPS Action 4. These interest limitation rules should be considered alongside transfer pricing regulations. In principle, net interest expense in the UAE is deductible only up to 30% of the borrower’s adjusted EBITDA. This rule only applies to cumulative interest expense greater than AED 12 million in a given year.

Article 28(1)(b) provides further rules that should apply specifically to intercompany arrangements. In addition to the foundational rule, any intercompany interest expense is non-deductible if:

• The financial arrangement lacks economic substance or commercial purpose; and
• The lender is not subject to a corporate tax rate of more than 9%; and
• The main purpose or one of the main purposes of the loan was to obtain a tax advantage.

Each of these must be proven for any interest deductions to be denied. These rules function to mitigate intergroup profit shifting and hybrid arrangements.

Zanders Transfer Pricing Software as a tool:

As tax authorities intensify their scrutiny, it is essential for companies to carefully adhere to the recommendations outlined above.  

Does this mean additional time and resources are required? Not necessarily. Technology provides an opportunity to minimize compliance risks while freeing up valuable time and resources. The Zanders Transfer Pricing Software is an innovative, cloud-based solution designed to automate the transfer pricing compliance of financial transactions.  

With over eight years of experience and trusted by more than 90 multinational corporations, our platform is the market-leading solution for intra-group loans, guarantees, and cash pool transactions. 

Our clients trust us because we provide: 

• Transparent and high-quality embedded intercompany rating models. 
• A pricing model based on an automated search for comparable transactions. 
• Automatically generated, 40-page OECD-compliant Transfer Pricing reports. 
• Debt capacity analyses to support the quantum of debt. 
• Legal documentation aligned with the Transfer Pricing analysis. 
• Benchmark rates, sovereign spreads, and bond data included in the subscription. 
• Expert support from our Transfer Pricing specialists. 
• Quick and easy onboarding—completed within a day! 

If you are interested in exploring how the Transfer Pricing Software could optimize your transfer pricing processes for financial arrangements, let us know in the contact form below.

The Challenge

The challenge is well known. Accounting conventions treat derivatives differently from commercial transactions. As a result, even when exposures are perfectly hedged, unrealised FX gains and losses on derivatives can create unwanted swings in net income. The fix is hedge accounting under IFRS and local GAAP—but designing policies, running effectiveness tests, documenting relationships, and producing audit-ready reports can stretch finance teams.

The Solution

Kantox Hedge Accounting Powered by Zanders automates this end to end. The software solution integrates out of the box with Kantox Dynamic Hedging, giving companies a true, end-to-end currency management automation setup that connects risk management and accounting seamlessly.

The benefits are immediate. Companies reduce operational cost and risk versus manual implementation, gain full technical and audit support, and remove net income variability stemming from unrealised FX gains and losses. Finance teams get consistent, audit-ready outputs while freeing resources to focus on higher-value activities.

Zanders & Kantox

Zanders brings deep expertise to the workflow. With more than 400 consultants, 12 offices across 4 continents, and 750+ customers, Zanders is a recognised authority in automated hedge accounting. Teams receive the level of support they need—from day-to-day assistance to premium, customised advisory—to draft hedge accounting policies, address country-specific audit requirements, and keep pace with evolving standards without building costly in-house capabilities.

By combining automation from Kantox with Zanders’ specialist guidance, organizations can implement hedge accounting with confidence—and finally align their FX risk management with their financial reporting.

Learn More

Find out about the full range of features and capabilities and watch the video on the Kantox website.

Interested in Kantox Hedge Accounting? Contact us to learn how to implement it quickly and smoothly.

Criminals never stand still, and neither should the models designed to catch them. Across the financial sector, institutions have invested heavily in advanced Financial Crime Prevention (FCP) models to detect fraud and money laundering. Yet the environment these models operate in is evolving faster than ever. As new technologies emerge and criminal behaviors adapt, yesterday’s patterns no longer predict tomorrow’s risks. 

Take cryptocurrencies: once niche, now mainstream. Their rise has transformed what “normal” transactions look like, blurring the line between legitimate activity and illicit movement. This shift underscores a growing challenge for banks—model drift. Without continuous monitoring and recalibration, even the most sophisticated FCP models can lose accuracy and allow financial crime to slip through the cracks. 

Model Drift, Data Drift, Concept Drift – What Are They? 

Model drift is defined as the degradation of the model’s performance over time. This can be due to many factors, such as sampling bias, but also due to data drift or concept drift. Data drift and concept drift are related and occur often at the same time, but tackle a different underlying issue.  

Data drift occurs when the distribution of data underlying your model changes. Take for example, bank A which acquires bank B. Such a takeover might change the underlying customer base significantly. Assuming that bank B has a higher risk appetite, these clients likely require different monitoring from the original customer base, e.g. by changing thresholds or developing new rules. 

Concept drift on the other hand means that a relationship that a model presupposes deteriorates or does not exist anymore. This can have large effects on the quality of model predictions. For example, criminals continuously develop new money laundering tactics to avoid being detected by ever-improving transaction monitoring models without impacting overall transaction distributions. This way the model still detects the outdated method the criminals used to apply but not the new methods. As a result, the model decreases in effectiveness. 

As mentioned above, data drift and concept drift often occur together. An example of these two concepts coming together is for the aforementioned cryptocurrencies. The distribution of cryptocurrencies have shifted significantly with more and larger transactions indicating data drift. In addition cryptocurrencies have gained a lot of popularity amongst criminals for developing new money-laundering schemes indicating concept drift. 

How To Monitor Model Drift 

Both concept and data drift can occur after the go-live of the model. It is crucial to have proper monitoring in place to timely be alerted. Generally, model monitoring frameworks include periodic review of a models’ effectiveness. Creating awareness for data drift and concept drift during this periodic review can create an alertness if the model performance or underlying distribution significantly shifts. Besides the regular assessment cycle, some monitoring thresholds can be upheld: 

• Data drift: measure the underlying distribution of risk drivers at model initiation. Significant distortions in this initial distribution should be bound to some pre-defined limit. Once these thresholds are breached, a review can be initiated to assess its contribution in erroneous predictions. An example of a metric that could be used for this purpose is the Population Stability Index (PSI), which measures the difference between the distributions of two different population samples. 

• Concept drift: measure the predictive power of the individual risk drivers against the dependent variable. If there seems to be significant deterioration of the explanatory power, a review of the model design can be initiated. For example by using SHAP-values, the individual contribution of a risk driver towards the general risk classification can be utilized. If these SHAP-values provide a decrease in explanatory power since model initiation, this can indicate concept drift. 

Prevent Drift From Derailing Your Models 

Besides a solid model monitoring framework and regular periodic reviews of the model, model drift should also be considered during model development. During model (re)development, the following points should be considered to counteract model drift: 

• Measure the sensitivity of the model against small changes in averages of your input. Assessment of the impact of changes in individual risk drivers can give insight into over-reliance towards specific characteristics that are prone to change.  

• Sensitivity can also be measured towards the change in distribution, such as flattening or skewing the distribution.  

• Assess the change to your model when risk drivers are removed. Here, again, the impact should be manageable.  

• Investigate the stability of your risk drivers over time.  

• Perform a qualitative analysis on the robustness of risk drivers. 

These steps give a solid base to include mitigants for data drift and concept drift into your model development cycle. 

This article is part of a larger series highlighting crucial topics for the future of financial crime prevention. See an overview of the whole series here. 

Creating a solid model development framework that includes model and data drift can be a challenge and requires deep domain experience and experience. If you need guidance in making your models future-proof, Zanders can help.  

Want to know how Zanders can support you in this transition? Feel free to reach out through our contact page to get in touch with an expert.  

AI is everywhere – but many treasurers still struggle to move beyond the hype and identify real, value-adding applications. That’s why in this blog series, we’ll explore four practical use cases in depth, each resulting in a concrete, actionable application you could begin implementing tomorrow. 

In this blog, we focus on one of the most time-consuming challenges in any treasury transformation: system testing. Testing remains highly manual, repetitive, and resource-intensive. But the rise of AI agents is changing the game, offering a way to make testing faster, more intuitive, and highly repeatable. 

The Challenge 

Testing is at the heart of every treasury transformation. It’s a meticulous, iterative process: before you even begin, you need a robust set of test cases that reflects your specific workflow to ensure every critical scenario is covered, without drowning in an endless list. Then comes the manual execution, documentation, and feedback cycles, repeated again and again until every issue is resolved. 

Even after go-live, the work isn’t over. Regular system updates and configuration changes mean more rounds of regression testing to catch any unexpected side effects. It’s essential work, but it can be a real drain on time and resources. 

Now, thanks to AI, there’s a smarter way. 

The Solution 

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AI agents are autonomous actors that can interact directly with your web browser and through that with your Treasury Management System (TMS) to complete tasks and resolve issues. Imagine instructing an AI agent in plain language, and watching it execute complex test cases right in the user interface. The agent is able to navigate to the right page, fill in information in the forms, the buttons, and record output. The flexibility of these agents even allow them to identify error messages and validate the result of a set of actions.  

Armed with vendor documentation, solution designs, and implementation blueprints, the AI agent proposes a comprehensive set of test cases and relates those to actions in your system of choice. Users can easily add their own scenarios by simply describing the workflow. The agent then navigates the system, executes the tests, and records the results, all autonomously. 

Worried about safety? Don’t be. Today’s test automation tools come with robust guardrails: 

• Test environments only: No risk to production. 

• Controlled user rights: The agent gets only the permissions it needs. 

• Plan mode: The agent presents its action plan for human approval before anything runs. 

With these safeguards, the risk of unintended actions is virtually eliminated—while the efficiency gains are game-changing. 

You can build AI agents in-house, but many organizations will benefit even more from cloud-based platforms. These solutions: 

• Lower deployment costs and speed up implementation 

• Offer ready-made integrations 

• Enable users to share successful test scenarios 

At Zanders, we’ve developed an AI-powered test suite that plugs directly into leading cloud TMS platforms—empowering treasurers to accelerate testing without sacrificing control. 

A Real-World Example 

Let’s make this tangible. Suppose you want to test a workflow for adjusting FX exposure and generating a report. The AI agent, using system documentation and blueprints, proposes relevant user flows. One test case might be: 
“Change the EUR/USD exchange rate to 1.12, create a new EUR/USD FX Forward with a six-month maturity, and run the FX Exposure report.” 

The agent executes these steps, records the results, and once the optimal route is captured, can repeat the test autonomously and even evaluate the outputs itself. Over time, you build a powerful, reusable suite of automated test scenarios for both transformation projects and ongoing regression testing. 

Conclusion 

AI in treasury is no longer a futuristic possibility, it’s a practical, high-impact technology that treasury teams can start leveraging today. Do you want to explore automated testing? Contact us! Are you interested in other concrete use cases, learn more about transforming your treasury operations for the digital age. 

In the pursuit of climate-aligned finance, financial institutions face a critical challenge: incomplete emissions data. While disclosure frameworks such as the EBA’s Pillar 3 ESG requirements, the ECB’s climate risk guidance, and the EU Corporate Sustainability Reporting Directive (CSRD) continue to expand, their scope remains fragmented. Therefore, financial institutions must often assess climate-related financial risks and align portfolios without full visibility into counterparties’ environmental footprints.  

In collaboration with a leading international bank, Zanders explored how machine learning can support more accurate, scalable, and decision-useful estimates of greenhouse gas (GHG) emissions intensity when disclosures fall short. 

The Challenge: Incomplete GHG Emissions Disclosure 

Current climate risk assessments rely heavily on firm-disclosed emissions. Yet, many companies, particularly small, private, or non-European, still do not report their GHG emissions. This inconsistency not only limits the accuracy of portfolio-level financed emissions metrics, but also hinders accurate net-zero alignment tracking and regulatory reporting. 

To fill this gap, many financial institutions resort to sector-average proxies, such as those recommended by the Partnership for Carbon Accounting Financials (PCAF). These proxies assign emissions to non-reporting firms based on average industry and regional emission intensities. While widely adopted, this approach introduces substantial bias, as it overlooks firm-specific drivers such as energy use, capital intensity, or geographic differences. The result is a blind spot: portfolio assessment loses the very granularity needed to distinguish leaders from laggards in the low-carbon transition. 

Predicting Emissions Intensity with Machine Learning 

The main objective of the study focused on testing various supervised ML models to estimate Scope 1 and 2 GHG emissions intensity based on a variety of financial firm-level characteristics. Leveraging an unbalanced panel dataset covering worldwide public and private companies from 2021 to 2025, models were trained to learn from disclosed emissions and predict missing values with greater granularity. The dataset was split into approximately 80 % training and 20 % testing subsets, ensuring that observations from the same company (across different years) did not appear in both sets to prevent information leakage. 

Two models were introduced: 

• Model 1, a baseline that includes financial and sectoral indicators widely available for banks, such as assets turnover; property, plant and equipment (PPE); earnings before interest and taxes (EBIT); and industry classification.  

• Model 2, an extended model that incorporates more advanced and less common variables such as Refinitiv ESG score; energy consumption; and earnings quality rankings. 

These predictors were selected based on both academic relevance and practical availability in financial databases such as LSEG Workspace (previous Refinitiv Eikon) and S&P.  

In both Model 1 and Model 2 settings, three algorithms were compared: k-Nearest Neighbours (k-NN), Decision Trees, and Random Forests, chosen for their interpretability and practicality in low-data environments. To assess whether machine learning provides a meaningful improvement over traditional sector-average proxies, both the ML models and PCAF sector-average proxy estimates were examined on a common test set. Unification of this comparison allowed for quantifying the overall predictive gains and evaluating the implications for climate-aligned decision-making in finance. 

Models performance was evaluated using standard regression metrics including Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), ensuring consistency across models and comparison with the baseline. Beyond standard error metrics, performance was also assessed through variance recovery (reported further as Median Variance Relative Gain). This measure captures how effectively each model restores firm-level differentiation in GHG emissions intensity lost under sector-average proxies.  

The entire framework was designed to balance predictive accuracy with implementation realism, aiming to improve GHG coverage for financial institutions without relying on black-box techniques or data-heavy infrastructure. 

What the Models Revealed 

Under each model and method, machine learning substantially outperformed the traditional PCAF proxy approach: 

The Random Forest version of Model 2 emerged as the strongest performer, reducing RMSE by roughly one-third, MAE by more than a half and recovering nearly 65% of the intra-sectoral variance lost under sector-average proxies. Model 1, created for banking sector usage, scored a second place under the same Random Forest algorithm, reducing RMSE by 21% and MAE by 41%. This means that the algorithm can effectively differentiate firms within the same industry, being a critical step for a realistic transition-risk modeling or portfolio creation. 

Feature importance analysis showed that Energy Use Total, PPE / Total Assets, Asset Turnover and Sector were consistently dominant predictors, confirming that emissions intensity depends jointly on operational efficiency and capital structure. However, the study also tested a transfer learning approach, where models trained only on high-disclosure sectors with sufficient reporting coverage were applied to low-disclosure sectors, unseen during training. The results showed a substantial decline in accuracy, suggesting that emission patterns are highly sector specific. In practice, this means that for ML models to exceed sector-average proxies in the GHG emission estimation context, models should be trained on datasets that include all sectors, rather than relying on samples limited to a few well-disclosing industries. 

Why This Matters 

More accurate emissions estimation directly supports key pillars of sustainable finance. It enhances portfolio alignment assessments, scenario analysis, and climate risk disclosure under frameworks such as Task Force on Climate-related Financial Disclosures (TCFD) and the EU Corporate Sustainability Reporting Directive (CSRD). Moreover, improved firm-level granularity enables financial institutions to better understand which clients are leading or lagging in the transition to a low-carbon economy. 

By replacing rigid proxies with data-driven predictions, financial institutions can move one step closer to climate data maturity, where decisions are no longer held back by disclosure gaps but empowered by intelligent estimation. 

What Zanders Can Do 

As regulatory expectations tighten and data coverage remains incomplete, financial institutions need solutions that are both technically rigorous and operationally feasible. Whether addressing climate-related credit exposures, integrating ESG into portfolio construction, or navigating disclosure obligations, institutions must adopt frameworks that are adaptive, data-driven, and aligned with supervisory standards. 

By combining quantitative modeling expertise, climate risk analytics, and regulatory knowledge, Zanders helps institutions move beyond generic estimates and static proxies. 

Want to find out more about how we can support you in building practical ESG risk management solutions? Our ESG experts will be happy to assist you. Visit the Zanders ESG page to know more.