Provider profile · 2026 edition
SHIELD review 2026: scores, pricing and alternatives.
A device intelligence provider used by e-wallets and apps in Asia to stop fake accounts and fraud at sign-up, login and transaction.
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A device intelligence provider used by e-wallets and apps in Asia to stop fake accounts and fraud at sign-up, login and transaction.
SHIELD scores 2.0 out of 5 (41 out of 100) in the Buckingham Intelligence Fraud Prevention Index 2026 and ranks 12 of 13. It is strongest for asian e-wallets and apps stopping fake accounts at sign-up and login, and its best criterion is integration at 4 out of 5. Not published or not stated on the pages read: coverage, pricing, detection signals, rules and case management and security and clients.
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 | 41 | 12 of 13 |
| For payment and e-money firms | 43 | 12 of 13 |
| For fintechs and marketplaces | 40 | 11 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.
Asian e-wallets and apps stopping fake accounts at sign-up and login.
What SHIELD publishes on its own pages, read on 26 October 2026.
A device intelligence provider used by e-wallets and apps in Asia to stop fake accounts and fraud at sign-up, login and transaction. It publishes adjustable signal sensitivity and integration "in minutes". It publishes no prices or certifications on the page read.
| Fact | What the provider publishes |
|---|---|
| Coverage | "Add device-powered detection at signup, login, transaction, and more"; identifies "fake accounts". Scams and chargebacks not published. |
| Pricing | Not published; pricing link leads to a demo request. |
| Signals | "Real-time device intelligence"; "Watch device behavior in real-time to flag when a good user turns bad"; "Fine-tune the sensitivity of 20+ fraud signals". |
| Operations | "Customize fraud policies that factor in behavior across the entire user journey". |
| Integration | "Integrate across web, mobile, and server-side environments in minutes, not weeks. Stream alerts via API and monitor everything in a unified dashboard." |
| Security and clients | No certification on the page read. Testimonial from Maya; logos include TrueMoney, Razer and Alibaba. |
| 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: shield.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 |
| DataVisor | 2.6 | 8 of 13 | Banks and credit unions wanting unsupervised machine learning and case tools |
| 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 |
| 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. SHIELD is compared with its alternatives in the table above.
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