> For the complete documentation index, see [llms.txt](https://doc.youverify.co/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.youverify.co/risk-scoring-and-compliance-health.md). # Risk Scoring and Compliance Health Risk scoring is Cowork’s expression of the **risk-based approach (RBA)** that AML and fraud management processes require: every entity gets a risk rating so you can apply proportionate due diligence and monitoring. It is a per-entity, operational score (distinct from Compliance Health, which measures your programme’s regulatory standing). ### How Risk Scoring works Cowork computes a single **entity risk score** from 0 to 100 using a **transparent**, **weighted model**. The score is the sum of weighted **factor categories**, and each category is built from underlying sub-factors. The default categories and weights are: {% columns %} {% column %}
{% endcolumn %} {% column %}
{% endcolumn %} {% endcolumns %} ### Weighted factor model | Factor category | Default weight | Example sub-factors | | --------------------------- | -------------- | -------------------------------------------------------------- | | Personal Factors | 35% | Identity verification, PEP screening, sanctions, adverse media | | Geographical Factors | 25% | Country, city, length of stay | | Product / Financial Factors | 20% | Industry, product exposure, alerts | | Behavioural Factors | 20% | Behavioural analysis | {% hint style="info" %} Worked example: Personal 8.50 + Geographical 17.77 + Product/Financial 6.00 + Behavioural 9.00 = **entity risk score 41.27 (Medium)**. A radar across the four categories shows where risk concentrates; a detailed breakdown expands each into its sub-factors. {% endhint %} Eachsub-factor contributes points toward its category; the category contributes its weighted share to the total. For example, an entity might score Personal 8.50%, Geographical 17.77%, Product/Financial 6.00% and Behavioural 9.00%, for a total **entity risk score of 41.27%**, which falls in the medium band. The platform also renders the score as a radar across the four categories so an analyst can see where the risk concentrates at a glance, and a detailed factor breakdown that expands each category into its sub-factors (e.g. “Country (Nigeria) 10.27%”, “PEP Screening 4.00%”). #### The score is dynamic, not point-in-time The risk score always reflects **current** data. The underlying screening sources, **country risk, PEP, adverse media and sanctions**, are **updated daily**, and **behavioural data is retrieved dynamically**, so every time an entity is scored the result is recomputed against the latest information rather than a stale snapshot. A customer who was clear last week but appears on a sanctions list or in adverse media today will score differently the next time the model runs, which is also what lets ongoing monitoring raise a signal the moment a previously clear entity becomes a hit (see 2.8). #### Risk bands The 0 to 100 score maps to **Low / Medium / High** risk bands. The band an entity lands in drives downstream behaviour (for example whether an AI-agent workflow auto-approves, escalates, or rejects).
#### Configurable from Cowork settings The risk model is **yours to tune**. From **Cowork settings → Transaction Settings → Rule Score Settings**, a customer sets the **score range for each band** (the Low / Medium / High boundaries). Category weights and sub-factor contributions are likewise configurable so the model reflects your own risk appetite and regulatory posture rather than a fixed vendor opinion. Because scoring is weighted and explicit, every score is explainable: you can show exactly which factors drove it. > Risk scoring drives operational decisions on a customer (what diligence, whether to approve, how closely to monitor). It is **NOT** the same as Compliance Health, which measures how well your programme meets regulators’ requirements. The two are deliberately separate. ### Evidence & confidence Risk and compliance signals carry a source type and confidence: **Platform** (automated, 100%), **Evidence** (uploaded document, \~80%), **Attested** (self-attestation, \~60%). Upload to the Evidence Vault or attest to raise a domain's score. ### Compliance Health: regulator audit & reporting (GRC) **Compliance Health is a governance, risk and compliance (GRC) tool, not a risk score**. Where risk scoring rates a customer, Compliance Health tells a Chief Compliance Officer how well the organisation complies with regulators’ requirements, and produces the evidence to prove it. It is built for **AML and regulator audit and reporting**, and works much like the GRC platforms firms use to evidence ISO and other audits, but purpose-built for financial-crime compliance.
It measures readiness across roughly a dozen baseline-standard domains using Youverify’s **patent pending DGE methodology**, three pillars each scored 0 to 100: {% hint style="info" %} Patent-pending status and the exact DGE methodology are Youverify-proprietary. {% endhint %}

Defensibility

AML solution, CDD/KYC/KYB, sanctions & PEP screening, fraud detection tools.
Are the right controls in place?

Governance

Policy documentation, board oversight, compliance officer appointment.
Is the programme properly run?

Effectiveness

How well detection and response are actually performing.
Are those controls actually working in practice?

A headline **platform-verified percentage**, a count of **critical issues**, and an **improvement streak** summarise overall standing. **The confidence/evidence model**. Each domain is backed by a signal whose strength depends on how it is substantiated, which is what makes the output audit-grade: * Platform (automatically verified by Cowork): highest confidence, treated as 100%. * Evidence (a document uploaded to the Evidence Vault): high confidence, treated as \~80%. * Attested (a self-attestation the officer records): lower confidence, treated as \~60%. A CCO raises a domain’s score and confidence by uploading evidence or recording an attestation, exactly as they would build an audit file. The result is a defensible, continuously-updated view of regulatory compliance that can be exported for an examiner or an internal-audit review. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://doc.youverify.co/risk-scoring-and-compliance-health.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.