Provider profile · 2026 edition
Oscilar review 2026: scores, pricing and alternatives.
A risk decisioning platform for fraud, credit, onboarding and AML, with device and behaviour signals, backtesting on historical data and case management.
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A risk decisioning platform for fraud, credit, onboarding and AML, with device and behaviour signals, backtesting on historical data and case management.
Oscilar scores 2.6 out of 5 (53 out of 100) in the Buckingham Intelligence Fraud Prevention Index 2026 and ranks 5 of 13. It is strongest for US fintechs and payment firms wanting backtesting and case management together, 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 | 67 | 3 of 13 |
| For payment and e-money firms | 52 | 8 of 13 |
| For fintechs and marketplaces | 41 | 9 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.
US fintechs and payment firms wanting backtesting and case management together.
What Oscilar publishes on its own pages, read on 26 October 2026.
A risk decisioning platform for fraud, credit, onboarding and AML, with device and behaviour signals, backtesting on historical data and case management. It shows ISO 27001 and SOC 2 badges and names regulated fintech and payment clients. It publishes no prices or integration details.
| Fact | What the provider publishes |
|---|---|
| Coverage | Detects "card testing, chargeback fraud", "suspicious logins" and abnormal sign-up behaviour; onboarding and AML. Scams not published. |
| Pricing | Not published on the page read. |
| Signals | "Next-generation device and behavioral intelligence"; device ID reuse checks; "Explainable decisions & full auditability". |
| Operations | "Easily create or modify workflows, and automatically backtest with historical data"; "context-rich case management"; AML transaction monitoring and sanctions screening. |
| Integration | Not detailed on the page read. |
| Security and clients | ISO 27001 and SOC 2 badges (SOC 2 type not stated). Named clients: dLocal, SoFi, Nuvei and Clara. |
| 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: oscilar.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 |
| 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 |
| 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.
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