Eval directory

Evals for Arva AI

Eval coverage for Arva AI, mapped from its public product surface.

About Arva AI

Arva AI provides enterprise-grade AI agents that automate financial crime compliance reviews for banks and fintechs. Its agents cover Screening, KYB/KYC, AML, and Transaction Monitoring, replacing manual L1 analyst review with context-aware automation and enrichment. The company is partnering with Fiserv to bring its agents to the banks and credit unions on Fiserv's agentOS platform.

Industry

financial crime compliance AI agents

Website

arva.ai

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We'll build out the full library — runnable test cases with inputs, expected behavior, and pass/fail checks — in your Corsac workspace.

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Coverage map

What would you measure for Arva AI?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

Screening review

Context-aware disposition of watchlist and sanctions alerts, where the product's stated differentiator is understanding names in context rather than string matching.

“Arva automates 92% of all financial crime reviews across Screening, AML, KYC/KYB, and more” arva.ai

Mapped capabilities

4 capabilities

Advanced name matching
Transliterations, aliases, and common names resolved in context rather than by string similarity.
Jurisdictional filtering
Applying jurisdiction and list-relevance filters to suppress irrelevant matches.
False-positive discharge
Clearing noise alerts with a stated reason instead of passing volume to analysts.
Enrichment via Arva Intel
Pulling supporting entity context to confirm or discount a candidate match.

Illustrative example

Input: Screening alert: customer "Mohammed Al-Sayed," UK resident, born 1991, matched to a sanctions entry for "Muhammad Alsayed," born 1962, Syria. No other identifiers overlap.
Expected behavior: Recognize the match as a transliteration-driven common-name collision, cite the disqualifying date-of-birth and jurisdiction mismatch as the basis, and discharge the alert as a false positive rather than escalating it.

KYB / KYC review

Structured business intelligence for onboarding: verifying who a business is, what it actually does, and who owns it.

“Arva reads and interprets the business's website, metadata, social links, and online footprint” arva.ai

Mapped capabilities

4 capabilities

Website and web presence analysis
Reading a business's site, metadata, and online footprint to test whether stated activity is credible.
Document intelligence and fraud detection
Extracting from submitted documents and flagging signs of tampering or inconsistency.
Ownership and registry reconciliation
Assembling ownership structure from incomplete or fragmented registry data.
Stated-vs-observed consistency
Detecting mismatch between applicant-declared activity and independently observed evidence.

Illustrative example

Input: KYB case: applicant declares "management consulting." Its live website sells nicotine vape products with a checkout, and the registry filing lists a retail activity code.
Expected behavior: Flag the mismatch between declared and observed activity, name the website and the registry code as the conflicting evidence, and escalate for human review instead of clearing the applicant on the declared classification.

Transaction monitoring review

Triage of high-volume TM alerts with the entity and behavioral context that legacy systems omit.

“AI understands names in context — handling transliterations, aliases, common names, and jurisdictional filters” arva.ai

Mapped capabilities

4 capabilities

Alert enrichment
Gathering entity information and supporting context before disposition.
Pattern assessment
Assessing flagged activity against plausible legitimate explanations for the customer profile.
Volume handling
Consistent behavior across large alert batches rather than per-alert drift.
Escalation packaging
Producing an investigation-ready summary for the alerts that do move on.

Disposition and escalation control

The decision boundary between what the agent closes automatically and what reaches a human, which drives both STP gains and residual risk.

Mapped capabilities

4 capabilities

Auto-close vs escalate boundary
Withholding an automated clear when evidence is thin, conflicting, or missing.
L1 replacement scope
Staying within L1-equivalent judgment and not pre-empting higher-tier decisions.
Human handoff quality
Handing a reviewer the case state and open questions needed to finish it.
Confidence calibration
Stated certainty tracking the strength of the underlying evidence.

Auditability and regulatory defensibility

Arva's stated requirement that AI here be reliable and auditable enough for regulators and institutions; covers whether decisions can be reconstructed and defended.

“1 M+ Reviews Handled Monthly Built for scale, Arva processes 100s of thousands of alerts a month” arva.ai

Mapped capabilities

4 capabilities

Evidence-linked rationale
Every disposition tied to the specific sources and fields that produced it.
Decision traceability
A reconstructable record of the review for internal audit or examination.
Claim discipline
Not asserting verification that the underlying data does not support.
Consistency across repeat reviews
Comparable cases receiving comparable dispositions.

Deployment and integration surface

How the agents reach institutions — direct integrations and the Fiserv agentOS distribution to banks and credit unions — plus the security posture the product markets.

Mapped capabilities

4 capabilities

Out-of-the-box integrations
Ingesting alerts and case data from existing screening and monitoring stacks.
agentOS distribution fit
Operating within the bank and credit union deployment context described for Fiserv agentOS.
Data handling posture
Behavior consistent with the security commitments made to regulated customers.
Degradation on missing inputs
Behavior when an upstream source or enrichment provider returns nothing.

Coverage is mapped from Arva AI's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Arva AI test?+

The coverage map is generated from Arva AI's own public product surface (financial crime compliance AI agents): 6 scoring areas — Screening review, KYB / KYC review, and Transaction monitoring review, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Arva AI evals scored?+

Every case generated for Arva AI — across Screening review and KYB / KYC review and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Arva AI library include?+

The full Arva AI library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Advanced name matching and Jurisdictional filtering under Screening review); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Arva AI or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Arva AI areas and set them up in a Corsac workspace, where you can run every test case against Arva AI or your own agent with your own data.