Adverse Media Matcher

Deduplicates and matches adverse media to customers.

Industry	Level	Difficulty	Recommended mode
Anti-money laundering	3 of 5	Advanced	flat

Coming soon

A complete, runnable Adverse Media Matcher reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Adverse media volumes are huge.
• Many articles repeat the same story.
• Matching names is error-prone.

How SrvDB powers Adverse Media Matcher

1. Articles are embedded and deduplicated.
2. Customer profiles retrieve relevant articles.
3. Multilingual embeddings span languages.
4. Results reviewed by analysts.

What changes

Today	With Adverse Media Matcher on SrvDB
Duplicate articles	Deduplicated stories
Name-only matching	Context matching
Language gaps	Multilingual

How it works

1. Ingest media.
2. Embed and deduplicate.
3. Match profiles.
4. Review.
5. Record decisions.

SrvDB features it uses

• search_batch: runs many queries at once, in parallel inside Rust.
• True cosine scores: scores in [-1, 1] mean the same in every mode, so thresholds are stable.
• upsert: inserts new items and replaces changed ones, so the knowledge base stays current.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.

Use cases

• Onboarding screening
• Periodic reviews
• PEP context
• Sanctions context
• Supplier screening
• Investigations

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./adverse-media-matcher-db", dimension=768)

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def relevant_articles(profile, k=5):
    hits = db.search(embed(profile), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Supplements, does not replace, screening systems.

Caution

Decision support only: SrvDB does not replace regulated screening systems or investigator judgement.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Anti-money laundering projects

Level	Project	What it does
1 · Beginner	Typology Library	Search money-laundering typologies and red flags by description.
2 · Intermediate	Alert Prioritizer	Ranks AML alerts by similarity to past true and false positives.
3 · Advanced	Adverse Media Matcher (this page)	Deduplicates and matches adverse media to customers.
4 · Expert	AML Investigation Agent	An investigator agent with memory across cases, typologies and media.
5 · Frontier	Emerging Typology Radar	Detects clusters of cases far from every known typology.

See also: Use cases · Live examples (coming soon)