Launching an Agentic-AI Adverse Media Risk Data API for KYC/KYB Teams
Adverse media screening is noisy, slow, and expensive when done with legacy tools or manual searches. Financial institutions and fintechs lose hours chasing false positives instead of focusing on real risk.
This launch introduces an adverse media Risk Data API that uses agentic AI workflows to continuously collect, enrich, and score news so you get cleaner risk profiles at lower cost—especially as an early adopter.
The Problem: Noisy Negative News
Compliance and risk teams know the pain:
- High false positives when clients have common names (90%+ noise in early tests with keyword-based systems)
- Manual review of repetitive articles across multiple publishers—the same fraud case republished 50 times creates 50 alerts
- Limited coverage or slow updates when relying on small internal teams
- Regulatory pressure for continuous monitoring, making purely manual processes unsustainable
The result? Compliance teams drowning in false positives, real risks slipping through, and screening costs spiraling upward.
Our Approach: Agentic AI Pipelines
Instead of a single model call or keyword search, the platform runs an agentic, multi-step workflow for each subject:
- Collection agents continuously crawl and refresh global news and online sources
- Resolution agents unify aliases, languages, and co-entities into a single profile (e.g., "John Smith," "J. Smith," and "João Silva" → one person)
- Analysis agents detect risk-relevant events (fraud, sanctions, enforcement) and build a time-ordered timeline
- Deduplication agents collapse duplicate coverage into single, well-sourced incidents with citations back to original articles
Why "agentic" matters: Autonomous agents continuously crawl, cross-reference, and validate adverse media from multiple sources, improving precision compared with basic keyword search. Agentic workflows handle multi-step tasks end-to-end (collect → resolve entities → deduplicate → classify risk → attach citations), which shortens turnaround time and keeps unit costs low.
Because the workflow is mostly autonomous, you get fresher, more consistent data with less human intervention and fewer blind spots.
Measurable Outcomes
What you actually get from switching to this API:
Fewer false positives: In early tests, 85% reduction compared to keyword-based screening through context-aware matching (birth dates, locations, co-entities)
Clearer timelines of risk events: See how risk evolves over years instead of isolated headlines—arrest → trial → conviction → sentencing, all linked chronologically
Relationship mapping: Discover connections between entities involved in the same risk events—co-defendants, business associates, family members—with relationship types and descriptions
Faster case review: 10x faster analyst review thanks to pre-deduplicated, timeline-organized data. Review incidents once, not dozens of times.
Better auditability: 100% auditable with source citations—each event links to original sources (URL, publisher, date) for audit and case documentation
Lower total cost per screened customer: Because the pipeline is mostly automated, early adopters get enterprise-grade coverage at a lower price point while models continue to improve on real-world data
What You Get from the API
The API returns a structured JSON risk profile per person or business that can plug directly into onboarding, periodic review, or transaction monitoring flows.
Key Characteristics
- High-precision matching using context (locations, dates, co-entities) to reduce false positives
- Event timelines that show how risk evolves over years instead of isolated headlines
- Cross-publisher deduplication so analysts review incidents once, not dozens of times
- Full provenance: each event links to original sources for audit and case documentation
- Structured metadata: countries, roles, industries, registration numbers for vessels/aircraft/organizations
- Entity relationships: connections between entities involved in the same risk events—relationship types (associate, director, co-defendant) with brief descriptions to reveal hidden networks
Developer Story
For developers, this is a simple REST+JSON API with:
- OpenAPI specification for easy integration
- Sample code in multiple languages
- Webhook support for change notifications (real-time monitoring)
- Free sandbox with sample profiles and test API keys
- Comprehensive schema documentation at /static/schema.html
Example use cases:
- Call the API from onboarding workflows to screen new customers
- Trigger background checks during periodic reviews
- Subscribe to webhooks to receive updates when a monitored profile changes
- Integrate into transaction monitoring for real-time risk assessment
Why Agentic AI Lowers Your Cost
Traditional data providers rely heavily on manual research and rule tuning, which keeps per-search pricing high.
By using agentic AI to automate the full pipeline—from discovery to enrichment to scoring—the marginal cost per additional subject is much lower, which is why early adopters can access this dataset at significantly reduced pricing while the system scales.
This cost structure is ideal for:
- High-volume fintechs that need to screen thousands of customers continuously
- RegTechs building KYC/KYB platforms for other companies
- B2B platforms (marketplaces, payment processors) screening merchants
- Banks and financial institutions looking to reduce compliance costs without sacrificing coverage
Early Access and Pricing
Founding Customer Pricing
For the early access phase, we're offering significantly reduced pricing to teams willing to integrate early and provide feedback.
Why this works:
- You get enterprise-grade adverse media data at startup-friendly pricing
- We get real-world feedback to improve models and coverage
- Early adopters lock in discounted pricing for a fixed period or volume
Pricing tiers:
- Free sandbox: Sample profiles, test API keys, full schema access
- "Early Adopter" paid tier: Locked-in discounted pricing for a fixed period or volume (reflects "beta" stage with closer support and influence over the roadmap)
- Enterprise plans: For regulated institutions that need SLAs, custom sources, or dedicated support
This is a solid founder strategy because:
- It rewards teams that are willing to integrate early
- Gives us real-world feedback to improve models
- Still leaves room to increase prices later as coverage and accuracy expand
- Creates urgency ("founding customer" plan with locked-in discount)
How Agentic AI Makes Data Better
Beyond cost, here's how multi-agent orchestration improves data quality:
- Cross-checking sources: Agents validate claims across multiple publishers before flagging as high-confidence risk
- Continuous re-screening without extra cost: Once a profile is created, agents automatically update it as new articles appear
- Automatic enrichment: Agents discover connections (associates, companies, co-defendants) that a manual team would miss
- Context-aware disambiguation: Agents use birth dates, locations, and professional context to distinguish between people with the same name
What's Next
We're actively expanding the platform with:
- Real-time monitoring: Webhook notifications when new adverse media appears for monitored entities
- Multi-language expansion: Currently processing English, Spanish, Portuguese, and Chinese sources
- Sanctions & PEP integration: Unified screening across adverse media, sanctions lists, and politically exposed persons
- Custom risk models: Train agents on your organization's specific risk criteria
Try It Yourself
If you are building KYC/KYB, merchant onboarding, or continuous monitoring workflows and want higher-quality adverse media data at startup-friendly pricing, request a sandbox key and try the API against your current process.
Your next customers are already being written about. The goal is to surface the right stories before they become your next risk event.
Get started:
- Sign up on our homepage to get a free sandbox API key
- Explore the API schema documentation
- Review our data pipeline overview
- Book a demo to discuss enterprise needs
About Praman Labs: We build AI-powered compliance infrastructure for financial institutions. Our mission is to make regulatory compliance more accurate, efficient, and auditable through agentic AI technology—delivering higher signal, lower manual work, and founder-friendly early pricing.