AI-Powered Risk-Based Enforcement
AI-driven tools for governments and businesses that need to find the products, transactions, and supply chains with the highest probability and consequences of non-compliance — and act on them proportionately, with limited resources.
NON-COMPLIANCE RISK MAP — PRODUCT EXAMPLE
Each point is a product. Position = non-compliance risk. Size = market exposure.
Who Is This For
If you regulate and inspect
Legal defensibility
When a non-compliant product causes harm, you need to demonstrate that your inspections were directed at the products carrying the highest non-compliance risk — not selected arbitrarily or by habit.
Resource accountability
Enforcement budgets are finite and scrutinised. A risk-based framework gives you a documented, auditable basis for every resource allocation decision — defensible to oversight bodies and management alike.
An improving system
Products, supply chains, and markets change fast. A risk-based enforcement system learns from each inspection cycle — updating probability signals and consequence profiles so targeting becomes more accurate over time, not static.
If you are regulated and inspected
Avoid fines and findings
Know where your highest non-compliance risk sits before the regulator finds it. A systematic risk assessment across your product or transaction universe lets you act on the right problems first — not after enforcement.
Don't burden good suppliers and clients
Applying the same scrutiny to every supplier, product, or transaction regardless of risk damages relationships and wastes capacity. Risk-based targeting concentrates effort on the high-risk tail — leaving low-risk partners undisturbed.
Use compliance resources efficiently
Compliance budgets are under pressure. A risk-scored view of your exposure tells you where each unit of compliance effort reduces risk most — so you can allocate time, testing, and oversight where the return is highest.
The Framework
Separate Assessment of Probability and Consequences
Every ResidualRisk.ai tool is built on a single methodology for management of non-compliance risk: separate assessment of the probability and consequences of non-compliance. The goal is never to inspect everything — it is to ensure that the most dangerous non-compliance is always found first, and that limited enforcement resources are concentrated where they reduce risk most effectively.
This methodology was developed within the UNECE Group of Experts on Risk Management and Market Surveillance (RAMS) over fifteen years of intergovernmental work, and is codified in UNECE Recommendation R, Recommendation S, and Recommendation V, and in the ITC/UNECE Guide for Border Regulators (2022). It has been applied across food safety, consumer product safety, customs, plant protection, and financial compliance including AML and fraud detection.
The AI layer does not replace the methodology — it operationalises it at a scale and speed that was previously impossible for most regulatory authorities.
Six Tools. One Framework.
Each tool addresses a specific enforcement challenge. All share the same risk logic, ensuring that outputs are consistent, traceable, and defensible — not black-box predictions.
Tool 01
To bring residual risk to a tolerable level, enforcement authorities and compliance departments need a systematic framework for deciding what to check, when, and how — so that inspections are driven by non-compliance risk, not by habit, political visibility, or available capacity.
This tool uses LLMs to design the interrelated risk management processes, transform regulatory or business objectives into risk criteria, and develop the data flows of a risk-based enforcement system tailored to the organization's objectives and product scope. Each key process is implemented as a dedicated AI assistant.
Produces a documented, auditable framework ready for institutional adoption.
Tool 02
This tool runs a non-compliance risk scoring pipeline across your product or transaction universe, generating a ranked inspection list where the most dangerous non-compliance surfaces first. Every score is traceable: evidence cited, reasoning visible, defensible under audit.
Critically — the scoring logic is yours. We help you define the risk criteria that reflect your regulatory mandate, your market, and your enforcement priorities. The AI implements your logic at scale, not a generic model.
Applies to: product regulators · customs and border agencies · online marketplace compliance · AML/fraud screening
Tool 03
Once a product has been sampled, the enforcement authority faces a second resource constraint: laboratory testing is expensive, time-consuming, and often cannot cover every applicable regulatory requirement. This tool selects the optimal set of laboratory tests for a given product, maximising the probability of detecting non-compliance within the available testing budget and resource constraints.
Tool 04
Border control agencies often operate in silos. Customs checks for smuggling and other customs-related risks. Plant protection checks for pests and diseases. Food safety checks for pesticides and other risks. There can be up to 30 agencies inspecting a single shipment — each independently, each without visibility of what the others are doing.
This tool implements a multi-agency non-compliance risk management pipeline: it routes each shipment to the relevant agencies, assesses it independently according to each agency's risk profiles, and resolves overlapping priorities into a single coordinated inspection plan — reducing duplication, minimising delay, and ensuring the highest-risk aspects of every shipment are addressed first.
Tool 05
Online marketplaces have transformed product distribution — and the enforcement challenge that comes with it. Unsafe products can be listed, sold, and replaced faster than traditional surveillance can respond.
This tool deploys agentic AI to continuously monitor online market activity: scraping platforms, building non-compliance profiles for specific products, and feeding intelligence back into the risk targeting pipeline.
Tool 06
For platforms and regulators alike, ensuring product safety on online markets is no longer a question of conducting more inspections. The volume, speed, and cross-border nature of e-commerce requires a fundamentally different approach: systematic, intelligence-led, and risk-based.
This tool helps online marketplaces build a compliance framework that meets regulatory expectations and protects consumers — before harm occurs, not after.
Delivered in the field
Projects delivered through ITC, OECD, UNIDO, UNECE, and the World Bank — spanning border control, market surveillance, food safety, plant protection, consumer product safety, and financial compliance across Africa, Asia, Central Asia, the Middle East, and Europe.
Country-specific references available on request. Contact valentin@residualrisk.ai
About ResidualRisk.ai
PhD · Vice Chair, UNECE RAMS · Lead Risk Management Expert (OECD, ITC)
Key Publications
I design and implement AI-driven, risk-based regulatory compliance and enforcement frameworks — helping governments and businesses build systems that identify the most dangerous non-compliance, whether in products, transactions, or supply chains, target limited resources effectively, and reduce non-compliance risk to a tolerable level without creating unnecessary costs.
Since 2010, I have been developing intergovernmental risk management methodologies within the UNECE Group of Experts on Risk Management in Regulatory Systems, including methodologies for risk-based targeting and integrated risk management in border control. I currently serve as Vice Chair of the Group of Experts on Risk Management and Market Surveillance (RAMS), having led the development of foundational UNECE Recommendations and publications in this field.
As an international expert at ITC, OECD, UNIDO, and UNECE, and as an independent consultant, I have delivered projects in more than 20 countries covering food safety, consumer product safety, plant protection, border control, autonomous vehicle safety, and financial compliance, including fraud and AML.
I am currently developing and implementing AI-powered tools for targeting dangerous non-compliance in border control, financial compliance, and market surveillance — applying machine learning and LLM-based pipelines to assess non-compliance risk and prioritize enforcement actions.
Let’s make residual risk measurable and tolerable!
Valentin Nikonov
Get in touch
Whether you are designing a compliance system, scoping a technical assistance programme, or looking to pilot AI-powered risk targeting in your agency — start with a conversation.