AI in pre-signature verification is changing how transaction risks are managed. Instead of relying on outdated, static rule-based alerts, this approach uses real-time analysis to flag risks and provide actionable insights before funds are moved. The key idea? AI acts as a copilot, assisting humans in decision-making, not replacing them.

Key Takeaways:

• AI evaluates risks before signing: It checks sanctions, detects unusual patterns, and scores counterparties and evaluates stablecoin risks in real-time.

• Human oversight remains critical: AI handles routine tasks, while humans make judgment calls on complex cases.

• Improved efficiency: AI reduces false positives, speeds up reviews, and provides clear explanations for flagged transactions.

• Policy-as-code integration: Governance rules are automated, ensuring compliance is enforced at the moment of decision-making.

This system blends AI's speed with human judgment, creating a balance that ensures secure, compliant, and efficient transaction approvals.

Understanding Agentic AI for Financial Crime Prevention

How AI Assesses Risk Before Signing

Building on the "copilot" concept, AI evaluates various risk factors before signing. This involves specialized agents working in real time to flag potential issues, assign severity scores, and refer decisions to human approvers when necessary. Here's how these agents contribute to the pre-signature risk assessment process.

Sanctions and Taint Screening

AI agents pull data from live sanctions lists, PEP (Politically Exposed Person) databases, and fraud detection signals using decentralized oracles and Trusted Execution Environment (TEE) data providers. When a payment intent is initiated, the system cross-checks the recipient's address against these databases. If the address is flagged - either for appearing on a sanctions list or being linked to risky entities - the transaction is either blocked outright or routed for manual review, requiring documented justification.

For perspective, the U.S. Financial Crimes Enforcement Network (FinCEN) processed nearly 25 million compliance reports in 2023, many filed after transactions had already settled. By conducting these checks beforehand, the system helps avoid costly reversals and potential regulatory fines.

Detecting Unusual Transaction Patterns

AI uses historical transaction data to create a dynamic baseline of normal behavior, analyzing factors like transaction amounts, frequency, timing, and relationships between parties. If a new payment deviates from this baseline - such as a large transfer made at an unusual hour - it triggers a review.

Stablerail's anomaly detection system, for example, evaluates variables like time-of-day, deviations from typical amounts, and payout patterns to flag suspicious activity. Risk scores are assigned, and anomalies are presented with context to aid decision-making. On the blockchain side, AI can spot unusual behaviors like reused nonces or sudden bursts of transaction activity.

Counterparty Risk Scoring

AI assesses counterparties by examining their address book status, contract verification, and transaction history. For example, payments to long-standing, verified vendors are likely to receive low-risk scores. On the other hand, payments to newly created smart contracts or unknown entities may raise red flags.

These evaluations combine structured transaction data with unstructured context using Large Language Models (LLMs). This approach allows the system to consider a transaction's intent within a broader business framework. If a counterparty's risk score falls below a set threshold, additional measures - like CFO approval or extra verification steps - are triggered.

After calculating risk scores, AI provides human reviewers with actionable insights, making the decision-making process straightforward and efficient.

Plain-English Risk Explanations

AI translates its findings into clear, evidence-based explanations, referencing specific policy clauses and timestamps. These explanations are displayed directly on the confirmation screen, simplifying multi-step approvals for signers.

"When stakeholders can't understand how an AI model concluded, trust weakens. Explainable AI (XAI) frameworks help rebuild transparency, offering plain-language logic paths and visibility into key decision variables." - MetricStream

This level of clarity is especially important in regulated industries, where risk managers must justify decisions to auditors and regulators. The system also assigns severity levels - Low, Moderate, or High - to help approvers quickly identify which issues need the most attention.

Policy-as-Code: Governance Meets Automation

AI's real-time risk alerts are just the beginning. To ensure that only compliant transactions move forward, a codified risk framework - policy-as-code - steps in as the next layer of defense. This approach transforms business rules and regulatory requirements into machine-readable logic, eliminating the need for manual checks or endless email chains. Finance teams can now automate decisions, halting or approving transactions before they’re even signed.

Defining Machine-Enforceable Rules

Policy-as-code allows governance rules to be written in languages like Rego or WASM, forming the basis of what some call "compliance-as-code". These rules are enforced at the moment a transaction is initiated, before any cryptographic signature is applied. That means the system can automatically block, delay, or flag actions based on predefined conditions.

For example, teams can set policies requiring extra approvals for transfers above certain thresholds, payments to unfamiliar addresses, weekend transactions, or specific token types. This clarity removes guesswork and ensures consistent enforcement.

The key benefit? Consistency at scale. Unlike manual reviews, which are prone to human error and limited by staffing, policy-as-code operates with the efficiency of compute power, applying the same rules uniformly across all transactions. In 2023, global compliance costs topped $274 billion, much of it spent on reactive measures like paperwork. By enforcing rules upfront, organizations can catch issues early, avoiding costly corrections later.

Once these rules are applied, AI compiles its findings into a detailed Risk Dossier for further review.

Risk Dossiers: AI Verdicts on Intent

When a payment intent is submitted, AI generates a Risk Dossier - a structured report that delivers a verdict: PASS, FLAG, or BLOCK, complete with supporting evidence. These dossiers consolidate security signals from various sources, presenting everything in one place for decision-makers.

For instance, Stablerail's system categorizes risks by severity: Low (Green) for no known issues, Moderate (Orange) for concerns that need closer inspection, and High (Red) for critical violations. Each dossier includes clear, plain-English explanations, referencing specific policy rules and timestamps. This transparency helps approvers quickly understand why a transaction was flagged and what action is needed.

Human Approvals and Audit Trails

While AI handles routine decisions, human oversight remains essential for high-stakes or ambiguous cases. These are routed to manual review, where approvers can either accept AI’s recommendation, override it with a documented justification, or request further verification.

"Human investigators remain firmly in the loop, empowered to review and refine drafts in a collaborative workflow that blends AI efficiency with domain expertise." - Naik et al., Arxiv

Every step of the process - from intent to final approval - is recorded immutably. These cryptographic receipts, called Policy Decision Records, link the policy version, inputs, and approver IDs to the transaction. This creates a tamper-proof audit trail that finance teams can present to auditors, boards, or regulators. Unlike traditional compliance methods that rely on sampling or reconstructed narratives, policy-as-code systems offer real-time enforcement with complete, verifiable documentation.

This blend of automation and human judgment showcases a copilot model, where AI enhances decision-making without replacing human control.

From Intent Creation to Execution: The Workflow

Building on the discussion of AI risk assessment, the transaction workflow highlights how risk analysis, human oversight, and MPC-based execution come together in a streamlined process. This approach replaces outdated, fragmented approval methods with a unified system that is both auditable and secure. Each step generates a tamper-proof record, meeting the demands of internal governance and external compliance.

Creating and Submitting Intent

Every transaction starts with an intent, which can be submitted through various formats - PDF, CSV, or API. Advanced data extraction tools automatically populate key fields, reducing manual input and minimizing errors. This flexibility allows organizations to integrate stablecoin payments into their existing processes without requiring teams to overhaul their workflows.

For instance, cutting-edge models can extract vendor details from invoice PDFs, cross-check bank account numbers against master databases, and flag inconsistencies with up to 99.8% accuracy. Stablerail's system, for example, reads invoice metadata and auto-fills intent fields. This seamless integration sets the stage for real-time risk assessment and governance, effectively connecting the decision-making process to execution.

AI Risk Assessment and Approver Review

Once the intent is submitted, AI steps in to conduct a detailed risk assessment. Using third-party data, the system flags anomalies and compiles a Risk Dossier with a verdict. Routine transactions are auto-approved, while flagged cases are escalated to senior approvers for review and documentation. The AI checks for issues like sanctions exposure, unusual patterns, and counterparty risks.

Approvers receive these results in a user-friendly format with color-coded severity levels: 🟢 Low for routine transactions, 🟠 Moderate for items needing closer scrutiny, and 🔴 High for critical risks. For example, transactions involving Politically Exposed Persons (PEP) or payments exceeding $10,000 on weekends are flagged for senior review. Approvers, such as CFOs, can either accept the AI's recommendation, override it with a justification, or request further verification. This human-in-the-loop approach maintains accountability while leveraging the speed of automation.

MPC Signing and Evidence Generation

Once approved, the transaction moves to execution, secured through advanced multi-party computation (MPC) signing protocols. Unlike single-key signatures, which create a single point of failure, MPC requires multiple independent parties to contribute cryptographic shares before the transaction can proceed. This method eliminates risks like rogue actors or compromised keys. Stablerail's MPC-based wallets ensure that no single entity, including the platform itself, can unilaterally authorize transactions - only designated team members can sign off.

The MPC signing process generates a tamper-proof audit receipt, binding every transaction element with cryptographic safeguards. Time-bound nonces are used to prevent replay attacks, and every detail - from the task ID to the merchant context - is securely tied to the transaction. This comprehensive evidence trail completes the governance process, turning a simple payment into a fully documented decision that can withstand scrutiny from auditors, boards, or regulators.

AI Pre-Signature Verification vs. Custody Tools

AI Pre-Signature Verification vs Traditional Custody Tools Comparison

Expanding on the improved risk assessment workflow, it’s essential to understand the difference between custody tools and AI pre-signature layers.

Custody tools are primarily designed for key management and digital signing. They secure private keys using methods like MPC (multi-party computation) or hardware security modules (HSMs). However, their scope is limited - they lack insight into the broader business context. For example, custody tools can’t determine if a vendor is legitimate, whether a payment violates internal spending policies, or if the timing of a transaction seems unusual. Essentially, custody tools focus on who can sign, not what is being signed or why it’s happening.

AI pre-signature layers, on the other hand, take a different approach. Instead of focusing solely on key security, they emphasize the intent and quality of the decisions behind each transaction. Acting as a "copilot", these layers assess factors like vendor history, invoice authenticity, payment terms, and behavioral patterns before a signature is requested. They highlight risks - such as exposure to sanctions, suspicious contracts, or irregular payment behaviors - directly within the approval process. This provides finance teams with the critical context that custody tools simply don’t offer.

Comparison Table: Custody Tools vs. AI Pre-Signature Layers

Here’s a quick breakdown of how these two approaches differ:

This table highlights a key shift: custody tools focus on managing signing authority, while AI pre-signature layers ensure business governance. For instance, frameworks like MAPL simplify policy complexity, reducing the need for extensive manual rules by using hierarchical logic. Tools like Stablerail can automatically enforce policies such as “New address payments over $5,000 require CFO approval + verification,” transforming compliance into automated, machine-executable processes rather than relying on manual oversight.

Research Findings on AI's Impact

Research backs up the advantages of AI-driven pre-signature verification, showing clear improvements in fraud prevention and operational efficiency.

For example:

• AI-powered signature verification models achieve 91% accuracy in identifying valid signers on financial documents.

• Platforms using AI reach 99.8% accuracy in converting unstructured invoice data into structured, validated fields.

• Studies on authenticated workflows for AI demonstrated 100% recall with zero false positives across 174 test cases.

These numbers highlight how AI can standardize verification processes and eliminate the subjectivity and fatigue that come with manual reviews.

"I think the time has come to leverage AI for addressing various challenges faced by CFOs such as data accuracy... and ensuring compliance." - Mike Vaishnav, CFO and Strategic Advisor

AI also addresses bottlenecks, identifies fraud that might go unnoticed by human reviewers, and generates detailed audit trails to meet regulatory demands. Distributed Policy Enforcement Points, for instance, can verify cryptographic proofs with sub-millisecond overhead. This ensures that robust governance not only strengthens compliance but also maintains operational speed.

Conclusion

AI-powered pre-signature verification is reshaping how stablecoin transactions are governed by enhancing human decision-making. Studies reveal that when AI works as a copilot rather than taking full control, organizations see tangible benefits. For example, teams report a 29–36% boost in early detection of suspicious activities, a 33% drop in queue backlogs, and a 41% faster first response time for major cases. These results stem from AI's ability to manage routine tasks efficiently while directing complex or unusual cases to the right people for final review.

This approach highlights the value of a shared risk management strategy. The "copilot, not autopilot" framework ensures a balance between robust security and informed decision-making. While custody platforms focus on securing keys and managing signing authority, they often lack the necessary context to evaluate whether a payment aligns with internal policies or the reasons behind it. AI pre-signature tools like Stablerail fill this gap by performing tasks such as sanctions screening, anomaly detection, and counterparty risk scoring before a transaction is approved. Importantly, these findings are presented in plain language, making it easier for decision-makers to understand what they are approving.

"AI handles volume and speed, humans handle guidance and exceptions." - Microsoft Copilot Studio

Human oversight remains critical for high-stakes decisions, ensuring that nuanced risks are addressed promptly. By combining AI's speed and efficiency with human judgment, compliance evolves from being a reactive process to a proactive safeguard. This approach ensures real-time protection, supported by senior-level approvals and a complete audit trail.

FAQs

What does “copilot, not autopilot” mean in pre-sign checks?

AI as a “copilot, not autopilot” means it plays a supportive role by identifying risks and offering clear, evidence-based insights during pre-sign checks. However, it does not act independently or make decisions on its own. Instead, humans stay in control, carefully reviewing and approving every action before anything is executed. This approach ensures accountability and keeps decision-making firmly in human hands.

How does AI flag risky payments before anyone signs?

AI helps spot risky payments before they're finalized by analyzing various risk signals and the transaction's context. It looks at details like the recipient's history, whether the contract is legitimate, and any unusual transaction patterns. For example, it flags anomalies such as unexpected amounts or payments to unfamiliar counterparties.

These evaluations include processes like sanctions screening, risk scoring, and behavioral analysis. By flagging potential issues, finance teams can carefully review transactions that seem suspicious. Think of it as a "copilot" - it supports decision-making but doesn’t take action on its own.

What is policy-as-code, and who manages it?

Policy-as-code involves setting up governance rules and operational policies in a machine-readable format that can be automatically enforced by systems. These rules - such as approval requirements or transaction limits - are typically defined by finance or governance teams and then embedded directly into systems.

The benefit? Consistent compliance. Non-compliant actions are flagged or blocked automatically before they’re finalized, ensuring policies are followed every time. Plus, it creates an auditable trail of all decisions, making it easier to track and review actions when needed.

Related Blog Posts

• Stablecoin Governance: Copilot vs Autopilot

• Policy Enforcement with Anomaly Detection in Stablecoin Payments

• Stablecoin Payments: Pre-Sign Risk Checks

• Stablecoin Risks: Prevention Before Signing