WhatsApp and Telegram +44 7563 115980
[email protected]
A fraud and AML platform covering account takeover, application fraud, scams and money mules, using device and behaviour signals, unsupervised machine learning and anonymised signals across industries.
DataVisor scores 2.6 out of 5 (52 out of 100) in the Buckingham Intelligence Fraud Prevention Index 2026 and ranks 8 of 13. It is strongest for banks and credit unions wanting unsupervised machine learning and case tools, and its best criterion is fraud coverage at 4 out of 5. Not published or not stated on the pages read: coverage, pricing, security and clients, detection signals and rules and case management.
Scores from the Fraud Prevention Index 2026.
No award in the Fraud Prevention Index 2026.
| Segment | Score out of 100 | Rank |
|---|---|---|
| For banks and lenders | 58 | 8 of 13 |
| For payment and e-money firms | 54 | 6 of 13 |
| For fintechs and marketplaces | 44 | 6 of 13 |
The score out of 5 is the overall score out of 100 divided by 20. Leader goes to the top three overall, Best for to the first in each segment, and Top rated for to a score of 5 out of 5 on a criterion. Awards cannot be bought.
Where it scores best, and where to check closely, as shown in each ranking.
Banks and credit unions wanting unsupervised machine learning and case tools.
What DataVisor publishes on its own pages, read on 26 October 2026.
A fraud and AML platform covering account takeover, application fraud, scams and money mules, using device and behaviour signals, unsupervised machine learning and anonymised signals across industries. It publishes rules, case management and SAR narrative drafting. It publishes no prices, certifications or named clients.
| Fact | What the provider publishes |
|---|---|
| Coverage | "Account Takeover, Application & Onboarding Fraud, Identity Fraud, Scams & Social Engineering, Fraud Rings & Money Mules"; serves "banking, fintech, payments". Chargebacks not published. |
| Pricing | Not published on the pages read. |
| Signals | "Device & Behavioral Intelligence - dEdge"; "Unsupervised Machine Learning"; "anonymized signals across industries". |
| Operations | "Rules, Features, & Workflow Automations"; "Investigations & Case Management"; "Analytics & Reporting"; "SAR Narrative Generation". |
| Integration | Integration guide and API references published; testimonial: "deploy strategies in just 15 minutes". |
| Security and clients | States it meets "rigorous global standards for security, privacy, and compliance"; no certification named. No clients named on the page read. |
| Date read | 26 October 2026 |
A fact that is not published is scored as not offered or, for a fee, as if it were high. It is never estimated.
Facts read on 26 October 2026 from: datavisor.com.
Each score with the evidence behind it.
Each criterion is scored 0 to 5 against the best published offer in the Fraud Prevention Index. 0 means not offered or not published. How it is scored
The other providers in the same ranking, ordered by score.
| Provider | Score out of 5 | Rank | Strongest for |
|---|---|---|---|
| SEON | 3.9 | 1 of 13 | Payment and fintech firms wanting a published monthly price and fast set-up |
| Stripe Radar | 3.0 | 2 of 13 | Firms already processing card payments with Stripe |
| Unit21 | 2.9 | 3 of 13 | US banks and fintechs needing fraud rules, investigations and SAR filing together |
| Fingerprint | 2.8 | 4 of 13 | Fintechs wanting device recognition with clear self-serve prices |
| Oscilar | 2.6 | 5 of 13 | US fintechs and payment firms wanting backtesting and case management together |
| Sardine | 2.6 | 6 of 13 | Fintechs and banks combining fraud, scams and AML in one system |
| Feedzai | 2.6 | 7 of 13 | Large banks needing scam prevention and onboarding risk on one platform |
| BioCatch | 2.5 | 9 of 13 | Retail banks fighting scams, mule accounts and account takeover |
| Ravelin | 2.2 | 10 of 13 | Payment providers and online merchants fighting card fraud and chargebacks |
| Sift | 2.1 | 11 of 13 | Fintechs and marketplaces wanting a large shared data network |
| SHIELD | 2.0 | 12 of 13 | Asian e-wallets and apps stopping fake accounts at sign-up and login |
| Bureau | 1.6 | 13 of 13 | Indian lenders and marketplaces stopping fake applications at onboarding |
Side by side on every criterion, fact and award.
Head-to-head comparisons cover pairs within the top five of each ranking. DataVisor is compared with its alternatives in the table above.
Tell us what you need and where you operate.