Methodology
This page documents how NoCall turns thousands of user reports into a reliable assessment for every phone number. We explain the exact risk-score formula, the danger levels, how we classify categories, how our artificial-intelligence analysis works and where the data comes from. We publish this methodology so that anyone can understand —and question— the decisions the system makes.
How the risk score is calculated
Every number in our database is given a risk score between 0 and 100. It is not a subjective judgement: it derives from a deterministic formula that is recomputed every time a new approved report arrives.
risk = min(100, reports × 5 + (verified ? 30 : 0))
Each approved report adds 5 points. If our team has verified the number as confirmed spam, an extra 30 points are added. The result never exceeds 100.
The verification factor exists to distinguish a number with many recent reports (which could be a one-off false positive) from a number a human reviewer has actively confirmed as abusive. That is why 20 unverified reports (100 points from reports, capped) and 14 verified reports (70 + 30) can reach similar scores by different paths.
When a number has been analysed by our AI and has approved community signals, the detected danger level also acts as a floor for the score: a number flagged as critical will never show below 90, a high one below 70, medium below 45 and low below 20, even with few reports. This way a clearly fraudulent pattern is not undervalued just because it is recent.
Danger levels
From the numeric score we classify each number into one of four danger levels. These are the same identifiers our database uses internally:
Low (bajo)0–39
Few reports or none. The number shows no clear signs of spam activity. It may be a legitimate line or an isolated, unconfirmed report.
Medium (medio)40–59
Several reports received. Caution is advised before answering or calling back. The default value when the signal is ambiguous.
High (alto)60–79
Numerous confirmed reports. High likelihood of aggressive telemarketing or repeated unwanted calls.
Critical (critico)80–100
Number verified as spam or a scam, or identified by the AI as fraud or impersonation. We recommend blocking it immediately.
Spam categories
Every report and every number is classified into one of seven categories. The category determines how the number is presented in the directory and is derived both from the user's report and from the later automated analysis:
- SPAM — Generic, unwanted commercial calls that do not fit a more specific category.
- TELEMARKETING — Telephone sales campaigns, usually telecoms or energy, that persist despite rejection.
- SCAM — Scams and fraud, including identity impersonation (banks, public bodies, fake tech support). The most serious category.
- DEBT — Debt-collection and recovery calls, often aggressive or aimed at the wrong person.
- HARASSMENT — Repeated calls intended to harass, intimidate or deliberately disturb.
- SURVEY — Unsolicited phone surveys, polls and market research.
- OTHER — Any other type of unwanted call that does not fit the above.
Artificial-intelligence analysis
Beyond the numeric score, an AI worker analyses numbers based on community-approved content: the comments and reports that have passed moderation. The AI never works with unapproved content, so no unreviewed contribution can influence the public analysis.
For each analysed number, the AI generates a structured set of information shown on the number's page:
The fields the analysis produces are as follows:
- Description — A natural-language summary of who seems to be behind the number and what they want.
- Complaint patterns — The most repeated complaint reasons across reports (for example, calls at unsociable hours or persistence after refusal).
- Tactics used — The specific techniques detected, such as pressure, false urgency or requests for personal data.
- Sector — The field the activity belongs to (telecoms, energy, debt collection, surveys, etc.).
- Detected company — The company or organisation the number appears to represent or impersonate, when it can be identified.
- Impersonation — An indicator of whether the number pretends to be a legitimate entity (bank, public administration, known brand).
- Recommended action — The final recommendation for the user: block, caution, ignore or safe.
This analysis is indicative and generated automatically from community contributions; it does not replace the user's own judgement nor constitute an accusation against any specific company.
Moderation: nothing is published without review
Quality control is the centrepiece of the methodology. Reports and comments do not appear in the public directory the moment they are submitted: they remain pending until an administrator approves them. Only then do they count towards the risk score, feed the AI analysis and become visible to other users.
This moderation gate serves two purposes: it discards false or malicious reports before they affect a number's reputation, and it ensures the AI only reasons over vetted information. The author of a pending report can see their own contribution, but no one else, until it is approved.
Data sources
Each number's assessment combines three independent sources:
- Community reports — The foundation of everything. Users report numbers from the app and the web, anonymously, with a category and a comment. After moderation, they are the main signal for the score.
- CNMC operator and prefix data — We use public data from the National Commission for Markets and Competition (CNMC) to identify the operator assigned to each numbering block and the line type (mobile, landline, premium rate).
- Company press releases (RSS) — We follow companies' newsrooms via RSS feeds to detect legitimate campaigns and reduce false positives when a real company is running mass communications.
