Knowledge Graph–Driven Risk Scoring

Go beyond siloed data. Connect the dots.

Nevora transforms fragmented data into structured risk intelligence using knowledge graphs purpose-built for compliance. By linking entities, events, and jurisdictions, we uncover hidden connections and deliver transparent, explainable risk scores — even when traditional data is limited.

Reveal Hidden Risk Networks

Our AI-powered graph models map counterparties to adverse events, known associates, regulatory actions, and geographic risk — building a connected view of risk that surfaces what static reports often miss.

Built for Explainability

Every score is traceable to a network of relationships and source data. We don’t just say there’s a risk — we show where it comes from, why it matters, and how to validate it.

Tailored to Financial Crime Risk

Our graphs are aligned to real-world compliance needs — including AML typologies, PEP exposure, offshore structures, and enforcement history. That means better insights, not just better math.

Case Study: Strengthening Knowledge Graph Capabilities for Regulated Domains

Challenge

A company specializing in knowledge graphs for legal and regulatory content needed advanced NLP support to improve entity extraction and relationship mapping across complex, unstructured documents.

Solution

We were brought in to enhance key components of their pipeline — including entity linking, relationship modeling, and document normalization. The focus was on improving graph quality for high-stakes use cases such as compliance and legal discovery.

Impact

This work gave us hands-on expertise in structuring unstructured data for critical domains — experience we now apply independently to risk-focused graph building for AML and financial crime compliance.