Case Study — AstraFi

Compliance Automation Platform

Automating AML, KYC, and Regulatory Reporting for a Digital Asset Exchange

87% false positive reduction and same-day KYC for a $500M/month digital asset exchange

Industry

Digital Asset Exchange / Fintech

Timeline

18 weeks

Team

6 engineers

Tech

Python ML + PostgreSQL + Node.js

The Challenge

A digital asset exchange processing $500M monthly was manually reviewing AML alerts and submitting regulatory reports. The compliance team of 12 was reviewing 1,200 AML alerts weekly with an 85% false positive rate. KYC onboarding took 30 days. A regulatory deadline required SAR filing automation within 90 days.

Our Approach

How We Solved It

ML-Based AML Alert Scoring

Trained a graph neural network on transaction patterns and entity relationships to score AML alerts by risk level, reducing the alert queue from 1,200 to 180 genuinely suspicious transactions per week.

Automated KYC Orchestration

Built an automated KYC workflow that orchestrates identity verification, document OCR, sanctions screening, PEP checks, and adverse media search in parallel — reducing KYC cycle time from 30 days to 4 hours for 92% of applicants.

SAR Filing Automation

Automated Suspicious Activity Report generation from reviewed alerts, populating all required FinCEN SAR fields from the transaction data and analyst review notes, cutting SAR filing time from 3 hours to 12 minutes.

Regulatory Reporting Dashboard

Built a real-time compliance posture dashboard showing transaction monitoring coverage, alert queue status, KYC completion rates, and upcoming regulatory filing deadlines with automated submission workflows.

Engineering Process

How We Built It

Graph Neural Network for Transaction Patterns

Standard rule-based AML produces high false positive rates because it evaluates transactions in isolation. A graph neural network that models entity relationships and transaction network patterns identifies structuring and layering patterns invisible to rules.

Human-in-the-Loop by Design

Automated scoring reduces queue volume but does not auto-clear alerts. All final disposition decisions require analyst review and signature, maintaining regulatory defensibility and auditor confidence in the ML system.

Audit Trail as Core Infrastructure

Every compliance decision — alert disposition, KYC approval, SAR filing — is recorded with analyst identity, timestamp, supporting evidence, and reviewer sign-off in an immutable audit ledger.

Architecture Decisions

Key Technical Choices

Graph Database for AML Relationship Analysis

Neo4j graph database for entity relationship modeling versus PostgreSQL joins — multi-hop transaction relationship queries that take 45 seconds in PostgreSQL execute in 80ms in Neo4j.

Microservice KYC Orchestration Over Monolithic Workflow

Parallel execution of 7 independent KYC checks (identity, documents, sanctions, PEP, media, address, risk scoring) reduced KYC cycle time more than any individual check optimization.

Workflow Engine Over Custom State Machine

Used Temporal.io workflow engine for KYC orchestration — it handles retries, timeouts, human approval steps, and audit logging as first-class primitives without custom state machine code.

Results

What We Delivered

87%

False Positive Reduction

$500M/mo

Transactions Monitored

92%

SAR Filing Automation Rate

30 days → 4 hrs

KYC Cycle Time

Solution Blueprint

How It All Fits Together

Detection Layer

Graph neural network (AML)
Transaction pattern rules
Neo4j entity graph

KYC Orchestration Layer

Parallel 7-check verification
Document OCR pipeline
Sanctions & PEP screening

Reporting & Audit Layer

Automated SAR generation
FinCEN submission workflow
Immutable compliance audit ledger

Lessons Learned

What We Improved

False Positive Reduction Requires Regulator Agreement

Before deploying the ML scoring model, we met with the compliance team's regulatory counsel to agree on the acceptable false negative rate. Reducing false positives without regulatory alignment creates examination risk.

KYC Orchestration Is a Distributed Systems Problem

The 7 parallel KYC checks each have different SLAs, failure modes, and retry semantics. Using Temporal.io instead of a custom orchestrator saved 6 weeks of reliability engineering.

SAR Narrative Quality Requires Structured Analyst Notes

The first automated SARs had poor narrative quality because analyst review notes were unstructured free text. We redesigned the analyst review form with structured fields that map directly to SAR narrative sections.

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