Forced labor risk has become a critical issue for organisations operating across global supply chains, from banks and retailers to manufacturers and logistics providers. Increasingly, companies are expected not only to acknowledge these risks but to actively identify, manage, and mitigate them.
According to Moody’s, the challenge lies in the sheer complexity of modern business networks, where forced labor exposure can span multiple countries, industries, and layers of ownership, often hidden deep within third-party relationships.
Assessing forced labor risk is particularly difficult because it is rarely visible at the surface. Fragmented ownership structures, limited transparency around beneficial ownership in certain jurisdictions, and inconsistent global standards on forced labor and modern slavery all contribute to blind spots. For large organisations with extensive supplier and counterparty networks, this can make traditional, manual approaches to due diligence both inefficient and ineffective.
Without a robust, data-driven framework, organisations face a range of risks. Regulatory scrutiny around forced labor and modern slavery is intensifying globally, raising the stakes for non-compliance. Beyond potential fines or enforcement action, companies may also suffer reputational damage if links to forced labor are uncovered. Operational disruption is another concern, particularly where suppliers are suddenly deemed non-compliant or high risk, forcing rapid changes to sourcing or partnerships.
Moody’s Forced Labor Risk Assessment has been developed to address these challenges by providing a more structured and evidence-based approach to understanding exposure. Built in collaboration with the Rights Lab at the University of Nottingham, the assessment combines proprietary datasets, advanced analytics, and internationally recognised standards. The aim is to turn a complex ethical and compliance issue into actionable intelligence that can be embedded into decision-making processes.
The assessment can be applied at different stages of the business lifecycle. For some organisations, it may be used during onboarding to screen new suppliers or counterparties. For others, it forms part of ongoing risk monitoring, helping teams reassess exposure as ownership structures, operating geographies, or regulatory expectations change. In both cases, the goal is to provide a consistent way to quantify and interpret forced labor risk across business, industry, and country dimensions.
At the core of the approach is a multi-factor risk model that generates an overall forced labor risk score. This headline score is underpinned by three weighted sub-scores covering business risk, industry risk, and country risk. Each of these categories draws on a wide range of granular metrics, allowing organisations to drill down into specific drivers of risk and identify where mitigation efforts should be prioritised.
Complementing the model is the Forced Labor Risk Assessment Check, which operationalises the framework through an automated and structured process. By collecting data via surveys aligned to the three sub-scores, organisations can generate risk results that are categorised into clear risk zones, ranging from lower to higher risk. These zones can then be aligned with internal risk appetite and governance frameworks.
For organisations grappling with forced labor exposure, the benefits of this approach are largely about clarity and efficiency. A data-led assessment offers greater transparency across complex third-party networks and supports more targeted investment in mitigation. As forced labor remains a persistent global issue, tools that help organisations move from awareness to informed action are becoming an essential part of modern risk and compliance strategies.
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