Typology Library

Search money-laundering typologies and red flags by description.

Industry	Level	Difficulty	Recommended mode
Anti-money laundering	1 of 5	Beginner	flat

Coming soon

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

The problem

• Typology knowledge is spread across reports.
• New investigators lack experience.
• Red flags are described differently.

How SrvDB powers Typology Library

1. Typologies and red-flag descriptions are embedded.
2. Case descriptions retrieve matching typologies.
3. Local and confidential.
4. Updated with new guidance.

What changes

Today	With Typology Library on SrvDB
Searching reports	Instant typologies
Experience gaps	Shared knowledge
Inconsistent terms	Meaning-based

How it works

1. Collect typologies.
2. Embed.
3. Search.
4. Train staff.
5. Update.

SrvDB features it uses

• Exact search (flat): every query compares against every stored vector, so results are exact and explainable.
• 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.
• Fully offline: SrvDB runs in-process and never opens a network connection.

Use cases

• Trade-based laundering
• Structuring
• Shell companies
• Crypto patterns
• Real-estate laundering
• Mule networks

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("./typology-library-db", dimension=384)

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

Limits and honest notes

• Use alongside regulatory guidance.

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 (this page)	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	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)