BlazingAML: High-Throughput Anti-Money Laundering (AML) via Multi-Stage Graph Mining

TL;DR

BlazingAML introduces a scalable, multi-stage graph mining system for AML that captures fuzzy laundering patterns and significantly accelerates detection without sacrificing accuracy.

Contribution

It proposes a novel multi-stage framework and a domain-specific compiler to efficiently detect complex laundering schemes on CPU and GPU.

Findings

Achieves the same F1 score as state-of-the-art methods.

Provides 210x speedup on CPU and 333x on GPU.

Demonstrates superior scalability on IBM AML datasets.

Abstract

Money laundering detection faces challenges due to excessive false positives and inadequate adaptation to sophisticated multi-stage schemes that exploit modern financial networks. Graph analytics and AI are promising tools, but they struggle with the fuzziness of laundering patterns, which exhibit structural and temporal variations. Conventional data mining techniques require the detailed enumeration of pattern variants, which not only complicates the analyst's task to specify them, but also leads to large run-time overheads and difficulty training accurate AI models. The paper presents BlazingAML, a scalable AML system design that introduces: 1. A novel multi-stage framework for expressing fuzzy money laundering patterns 2. A domain-specific compiler that transforms high-level pattern descriptions into high-performance code for CPU and GPU back-ends The multi-stage abstraction decomposes complex laundering schemes into logical stages connected by graph operations, enabling diverse patterns to be expressed using unified primitives while capturing structural and temporal fuzziness. The compiler applies sophisticated optimizations, eliminating manual parallel programming requirements for financial analysts. Evaluation on IBM AML datasets shows BlazingAML achieves the same F1 score as state-of-the-art approaches while delivering 210x and 333x higher speedup on CPU and GPU respectively, with superior scalability.