Trusted BY LEADING
regulated institutions
Our Philosophy
Measurement beats intuition.
Rather than relying on increasingly powerful models alone, Arva combines proprietary benchmarks, continuous evaluation and real-world analyst feedback to ensure every improvement delivers measurable value before it reaches production. Research isn't a separate function—it's embedded throughout the lifecycle of every agent we build.
Proprietary Models
Proprietary Models
Generic AI benchmarks don't measure financial crime investigations.
We've built proprietary models from unique benchmark datasets from real-world investigations, edge cases and complex compliance scenarios to evaluate agents before they reach production.
Our datasets continue to evolve alongside emerging financial crime typologies, ensuring every new agent is tested against the problems our customers actually face—not academic benchmarks.
Continuous Learning
AgentCore
Great AI isn't static. AgentCore identifies recurring review patterns, proposes evidence-backed recommendations, validates every change through historical replay and tracks performance after deployment. Every recommendation follows the same process:
Observe → Evaluate → Validate → Deploy
Customers remain in control throughout, choosing which recommendations to apply while understanding the evidence and expected impact behind every change.
Insights
New
Archived
Approved
Turn on Countries discounting
17 Aug, 18:19
+6.3%
Configuration
Business agent
Turn on Article date discounting
10 Aug, 19:41
+16.5%
Configuration
PEP screening
Improve factor comparison
10 Aug, 19:41
+4.7%
Configuration
Business agent
Tighten name matching on adverse media (#12)
10 Aug, 19:41
+1.0%
Configuration
Payment screening v1
Closed Loop
Research doesn't stop after deployment.
Every analyst adjustment, escalation, or correction represents an opportunity to improve. We securely pipeline anonymized production feedback directly back into our evaluation datasets. This ensures Arva agents grow sharper in the specific context of your institution's risk appetite.
The result is a continuous research loop where operational learning directly improves future agent performance.
Capabilities
Domain intelligence built for financial crime.
We build specialized domain reasoning layers on top of base LLMs. This architecture powers deeper lookups and precise verification across complex structures.
Entity Resolution
Cross-checks disparate phonetic matches, local aliases, and typos to resolve identities.
Risk Enrichment
Instantly scrapes global sanction registry listings, deep corporate maps, and localized PEP registers.
Adverse Media
Translates and processes media in over 60 languages with context-aware sentiment analysis.
Beneficial Ownership
Traverses layered legal entities to find the Ultimate Beneficial Owners (UBO).
Cross-Source Intelligence
Correlates transaction anomalies with external investigative journalist registries.
Arva combines specialized financial crime intelligence with modular AI reasoning to eliminate noise safely.
AgentCore
Continuous optimisation for AI agents.
The result of Arva’s approach and use of AgentCore is a continuous improvement loop where every analyst decision contributes to making future AI decisions more accurate, more explainable and more reliable.
Customers remain in complete control, choosing which optimization suggestions to apply to their dedicated tenant sandboxes.
Disciplines
Our research is focused on five core disciplines.
Evaluation & Benchmarking
Developing statistically robust methodologies to map neural logic directly back to compliance workflows.
Agent Reasoning
Refining multi-agent consensus planning and state machine architectures for long-horizon compliance tasks.
Domain Intelligence
Compiling domain-specific data schema lookups to inform LLM generation with clean ontological clarity.
Continuous Optimisation
Automating hyperparameters and prompt engineering updates securely from secure, sandboxed telemetry.
AI Governance
Pioneering explainable verification logic to satisfy model risk regulations (SR 11-7) reliably.
Better benchmarks
create better agents.
Every benchmark makes our agents better.
Every analyst decision makes our benchmarks stronger.
Every improvement is measured before it reaches production.
That's how we build AI that financial institutions can trust.