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Artificial Intelligence is rapidly transforming the way organizations make legal, regulatory and risk-related decisions. Large Language Models are increasingly used to review contracts, interpret regulations, support compliance programs, assess operational risks, analyze financing requests, evaluate insurance claims and assist legal professionals in complex decision-making processes. The emergence of specialized Legal AI platforms such as Harvey has demonstrated the extraordinary potential of AI to augment legal work. At the same time, recent benchmark initiatives have highlighted an important reality: evaluating AI systems on academic exercises alone is no longer sufficient. The next generation of AI systems will not be measured by the amount of legal knowledge they can retrieve. They will be measured by their ability to reason. This raises a fundamental question: How do we evaluate whether an AI system can reason like a legal, compliance, risk or governance professional operating in the real world? ContractZLab Benchmark was created to answer this question. Our ambition is simple: To build the reference benchmark for Legal, Regulatory and Risk Reasoning across Europe, Africa and emerging regulated markets.
Over the past several years, the Legal AI ecosystem has produced significant advances in benchmarking and evaluation. Academic initiatives such as LexGLUE, LegalBench, MMLU-Law and Bar Examination Benchmarks have substantially improved our ability to measure legal knowledge and task-specific performance. At the industry level, platforms such as Harvey have demonstrated the importance of evaluating AI systems on realistic legal workflows rather than solely on academic datasets. These initiatives have accelerated the development of Legal AI and contributed to the emergence of increasingly capable systems. However, an important gap remains. Most existing benchmarks evaluate discrete legal tasks: • Legal question answering • Document classification • Information extraction • Legal retrieval • Statutory interpretation • Examination-style reasoning Yet this is not how legal, compliance and risk professionals operate in practice. Real-world decision making requires a combination of: • factual analysis, • legal interpretation, • regulatory assessment, • risk evaluation, • scenario exploration, • governance considerations, • professional judgment. Organizations rarely ask: "What is the correct article?" They ask: "Should we approve this supplier?" "Can this financing request be accepted?" "What are the compliance risks?" "Can this claim be challenged?" "What regulatory obligations apply?" "What is the safest course of action?" These are fundamentally reasoning problems. ContractZLab Benchmark was designed specifically to evaluate this capability.
The central hypothesis behind ContractZLab Benchmark is straightforward: Professional competence cannot be reduced to legal knowledge. A professional does not simply retrieve legal information. They: • qualify facts, • identify legal and regulatory issues, • determine applicable rules, • evaluate competing interpretations, • assess operational risks, • formulate recommendations, • justify decisions. This reasoning process sits at the center of modern legal, compliance and risk functions. For this reason, ContractZLab focuses on evaluating reasoning rather than memorization. The benchmark is designed to measure how AI systems think, not merely what they know.
Unlike traditional legal benchmarks, ContractZLab combines multiple categories of real-world professional scenarios. The objective is to evaluate AI systems across the full spectrum of legal, regulatory and risk activities encountered by modern organizations.
The benchmark incorporates real judicial and administrative cases extracted from multiple jurisdictions. These cases evaluate formal legal reasoning under realistic legal conditions and expose models to different legal traditions and interpretative frameworks. Current jurisdictional coverage includes: • France • European Union • Morocco • Switzerland • Saudi Arabia Additional jurisdictions will be introduced through future benchmark releases.
Modern organizations operate within increasingly complex regulatory environments. To capture this reality, ContractZLab includes scenarios involving: • regulatory obligations, • governance requirements, • compliance controls, • policy interpretation, • risk mitigation programs, • internal control frameworks. These scenarios evaluate a model's ability to transform regulatory requirements into operational recommendations.
A significant portion of legal work takes place outside courts. Corporate legal departments, procurement teams, risk functions, compliance officers and governance professionals spend their time supporting operational decisions rather than litigating disputes. To reflect this reality, ContractZLab integrates business-oriented use cases derived from real operational workflows implemented through the ContractZLab platform. Coverage includes: Procurement and Supplier Management • supplier onboarding, • supplier due diligence, • procurement governance, • supplier risk assessment, • contractual approval workflows. Contract Lifecycle Management • contract review, • contractual risk analysis, • clause assessment, • obligation management, • negotiation support, • approval processes. Credit and Financing • financing requests, • collateral assessment, • guarantees analysis, • credit committee support, • lending decision workflows. Insurance and Claims • claims analysis, • coverage assessment, • liability evaluation, • insurance compliance controls, • dispute scenarios. Risk and Compliance • regulatory gap assessments, • internal controls, • governance reviews, • remediation planning, • compliance investigations. These scenarios represent the day-to-day reality of organizations operating in regulated sectors such as banking, insurance, telecommunications, energy, public administration and large enterprises.
The IRAC methodology remains one of the most widely used frameworks for legal analysis. It structures reasoning through four stages: • Issues • Rules • Application • Conclusion IRAC provides an excellent foundation for evaluating legal reasoning. However, real-world legal and regulatory matters rarely produce a single deterministic answer. Facts may be incomplete. Multiple legal qualifications may coexist. Regulatory interpretations may differ. Several outcomes may be legally defensible. This reality is reflected daily in legal opinions, compliance assessments, risk reviews and due diligence reports. For this reason, ContractZLab extends traditional IRAC through a structured hypothetical reasoning framework. In addition to producing a primary analysis, evaluated models must demonstrate their ability to: • identify uncertainty, • formulate alternative scenarios, • evaluate conditions supporting each scenario, • assess potential consequences, • maintain appropriate professional caution. This capability reflects how experienced legal and compliance professionals operate in practice. The objective is not to reward certainty. The objective is to reward responsible reasoning under uncertainty.
Most publicly available legal benchmarks originate from ecosystems primarily focused on common-law jurisdictions and law-firm-centric workflows. Organizations operating across Europe and Africa face a different reality. They must simultaneously navigate: • civil law systems, • mixed legal systems, • European regulations, • banking regulations, • insurance requirements, • public sector governance obligations, • data protection frameworks, • emerging AI regulations, • cross-border compliance requirements. Many organizations must manage these obligations across multiple countries, multiple regulators and multiple languages. ContractZLab was designed specifically for these environments. Its objective is to evaluate whether AI systems can operate safely and effectively within the legal and regulatory complexity that characterizes Europe, Africa and other emerging regulated markets.
ContractZLab focuses on the dimensions that directly impact professional outcomes. Among others, the benchmark evaluates: • factual qualification, • issue identification, • legal rule extraction, • regulatory interpretation, • analytical consistency, • reasoning quality, • hypothesis generation, • practical recommendations, • jurisdictional compliance, • hallucination resistance. Particular attention is given to failures that create material professional risk. Examples include: • fabricated legal citations, • unsupported regulatory conclusions, • omitted legal conditions, • contradictory reasoning, • excessive confidence, • failure to identify critical risks. The benchmark is designed to expose these weaknesses systematically.
The future of AI in regulated industries will not be defined solely by model size or benchmark scores. It will be defined by trust. Organizations need systems capable of supporting high-consequence decisions responsibly. This requires evaluation frameworks that move beyond legal knowledge and measure reasoning in realistic professional environments. ContractZLab Benchmark was created to contribute to that objective. Because in law, compliance and risk management, knowing the rule is only the beginning. Understanding how to apply it responsibly is what truly matters.
This publication represents the first public overview of ContractZLab Benchmark. Future scientific publications will provide additional information regarding: • dataset construction methodology, • benchmark architecture, • evaluation framework, • scoring methodology, • benchmark statistics, • model evaluations, • jurisdiction-specific analyses, • reasoning alignment techniques. Our objective is to contribute to the development of more reliable, transparent and professionally usable Legal, Regulatory and Risk AI systems. Scientific publication and benchmark release coming soon.