Here’s a concise, practical catalog of modern tools and vendors you can use for adaptive authentication and fraud detection — grouped by capability, with quick notes on what they do best, typical use cases, and integration/selection tips.

Identity / Adaptive Authentication platforms (contextual, risk-based MFA)

• Okta Adaptive MFA — Contextual, device- and signal-driven adaptive MFA with policy engine, step-up and phishing‑resistant factors (FIDO2, passkeys), and integrations into SSO ecosystems. Good for workforce + customer identity use cases and fast cloud deployments. (Okta.com)
• Microsoft Entra ID (Azure AD) Conditional Access — Conditional access and risk-based controls integrated with Microsoft ecosystem (Azure, Office 365), strong if you’re already on Microsoft. (See vendor docs for current features and licensing.)
• Ping Identity / ForgeRock — Enterprise-focused identity platforms with configurable adaptive authentication flows and device posture hooks; suited to large enterprises, complex on‑prem/cloud hybrids.

Behavioral biometrics and device intelligence (detect bots, account takeover, social engineering)

• BioCatch — Behavioral-cognitive biometrics that detect human intent, coercion, mule behavior and session anomalies in real time; widely used by banks to reduce ATO and social-engineering losses. Good when you need deep session/behavior signals. (BioCatch.com)
• BehavioSec / NuData (Mastercard/others) — Keystroke, mouse/touch patterns and device signals for account‑level risk scoring.

Fraud decisioning / risk engines and global intelligence networks

• Sift — Machine‑learning fraud decisioning (RBA), identity graph and risk scoring used across e‑commerce, onboarding and ATO protection; strong ML models, fast decisioning and analyst tooling. Widely recognized in industry reports for fraud/RBA. (globenewswire.com)
• ThreatMetrix (LexisNexis Risk) — Large cross‑industry digital identity network, behavioral/device signals and explainable risk models; strong for payments, new‑account opening, cross‑channel detection. (risk.lexisnexis.com)

Specialized fraud prevention (commerce / payments / account opening)

• Forter / Kount (Equifax) / Riskified — Decisioning platforms targeted at e‑commerce transactions, chargeback protection and order‑fraud prevention; choose based on industry fit, chargeback guarantees, and integration with your payments stack.
• Arkose Labs — Focused on stopping fraud by differentiating bots/attacks from legitimate users; uses risk-based challenges to divert attackers while minimizing friction for real users.

Document verification / identity proofing

• Onfido, Jumio, ShuftiPro — ID document checks + liveness and KYC flows for onboarding. Useful combined with behavioral and device signals for stronger new‑account fraud control. (Check compliance features for your jurisdiction.)

Fraud orchestration / analyst tooling / investigation

• Fraud.net, DataVisor — Platforms that combine signals, alerting and orchestration across multiple fraud controls; useful when you need a unified investigations console and case management.

How these components are commonly combined

• Adaptive authentication = identity platform (Okta/Azure/Ping) + risk engine (Sift/ThreatMetrix) + behavioral/device signals (BioCatch/BehavioSec) to make real‑time step‑up/deny decisions.
• E‑commerce/Payments = fraud decisioning (Sift/Forter/Kount) + device intelligence + chargeback guarantees + optional manual review queue / orchestration.

Key selection criteria (quick checklist)

• Signals: Which signals do you need (device fingerprint, IP reputation, behavioral biometrics, transaction history, identity graph)?
• Latency & throughput: Real‑time (ms) decisioning required? Can vendors meet your peak traffic?
• Integration: Prebuilt connectors (SSO, SDKs, payment gateways, mobile SDKs, SIEMs)?
• Explainability & model governance: Need transparent rules vs. black‑box ML?
• Privacy & compliance: Data residency, PII handling, consent and local laws (GDPR, CCPA, GLBA) matter.
• Cost model: Per‑transaction, per‑monthly active user, or outcome-based (chargeback guarantees)?
• Operational tooling: Console for analysts, feedback loops for model retraining, false‑positive control.

Practical deployment path (recommended)

1. Map critical flows (login, password reset, new account, money movement, checkout).
2. Start with low-friction signals and a risk policy: IP, device posture, geolocation, velocity. Deploy risk-based step-up for high‑risk flows.
3. Add behavioral/device intelligence where ATO or social engineering losses are material.
4. Layer a decisioning engine (Sift/ThreatMetrix) to centralize scores and policies; expose actions to identity provider (block, step‑up, challenge, allow).
5. Monitor, tune thresholds and create analyst workflows to review edge cases and feed outcomes back into models.

If you want, I can:

• Recommend 2–3 vendors from the lists above tailored to your tech stack (e.g., cloud native, Microsoft-first, or bank/regulatory constraints).
• Draft a one‑page decision matrix comparing cost models, signal coverage, and integration effort for your top 3 choices.

Sources and further reading (examples)

• Okta Adaptive MFA product page and docs. (Okta.com)
• ThreatMetrix / LexisNexis Risk product overview (digital identity network + risk engine). (risk.lexisnexis.com)
• BioCatch behavioral biometrics and market recognition. (BioCatch.com)
• Sift product and industry recognition (fraud detection / risk‑based authentication leader). (globenewswire.com)

Would you like a short vendor shortlist matched to your environment (cloud vs on‑prem vs Microsoft‑centric) and prioritized by ease of integration and likely ROI?