IBM / AML-DataLinks
The data represents financial transactions -- bank transfers, purchases, credit card transactions, checks, etc. Most of the transactions are legitimate. A few represent money laundering. The data is in CSV format. The data is generated using a multi-agent virtual world model. All of the agents in the virtual world have actions governed by s…
☆55Updated last year
Alternatives and similar repositories for AML-Data
Users that are interested in AML-Data are comparing it to the libraries listed below
Sorting:
- The AMLSim project is intended to provide a multi-agent based simulator that generates synthetic banking transaction data together with a…☆295Updated 2 months ago
- Multi-GNN architectures for Anti-Money Laundering.☆85Updated last year
- Repository for Fraud Dataset Benchmark☆193Updated last year
- This repository builds an anti-money laundering scheme employing graph theory and social network analysis to detect the sophisticated lay…☆20Updated 5 years ago
- Anti Money Laundering Detection using Graph Attention Network☆53Updated last year
- Financial Simulator of Mobile Money Service☆109Updated 4 years ago
- AML End to End Example☆55Updated 3 years ago
- Colab implementation for Fraud Detection in Graph Neural Networks, based on Deep Graph Library (DGL) and PyTorch backend.☆41Updated 4 years ago
- Curated list of Graph Neural Network Applications in Business, Finance and Banking☆95Updated last year
- This repository contains the code used in the experimental setup of the paper 'Inductive Graph Representation Learning for Fraud Detectio…☆33Updated 2 years ago
- ☆10Updated 3 years ago
- Accompanying repository for my book about Graph Data Science☆80Updated 2 years ago
- Fraud detection with the Paysim financial dataset, Neo4j Graph Data Science, and Neo4j Bloom☆46Updated 9 months ago
- Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Wo…☆88Updated 5 years ago
- Graph Machine Learning, published by Packt☆286Updated last week
- Exploring Neo4j and Graph Data Science for Fraud Detection☆74Updated 2 years ago
- DYnamic MOtif-NoDes (DYMOND) is a dynamic network generative model based on temporal motifs and node behavior.☆14Updated 2 years ago
- Node Embeddings in Dynamic Graphs☆57Updated 3 years ago
- Synthetic Credit Card Transaction Generator used in the Sparkov program.☆153Updated 3 years ago
- Automatic feature extraction and node role assignment for transfer learning on graphs (ReFeX & RolX)☆87Updated last year
- Record matching and entity resolution at scale in Spark☆35Updated last year
- Generator for graph of transactions. Is kind of optimized for large graph generations. Contains graph structure generation, nodes informa…☆28Updated last year
- An end-to-end blueprint architecture for real-time fraud detection(leveraging graph database Amazon Neptune) using Amazon SageMaker and D…☆219Updated last year
- Hands-On Graph Analytics with Neo4j, Published by Packt☆92Updated 2 weeks ago
- Paper collection for graph based models in finance application☆106Updated 4 years ago
- Implementation of feature engineering from Feature engineering strategies for credit card fraud☆41Updated 4 years ago
- ☆30Updated 4 years ago
- ☆31Updated 2 years ago
- Simulation framework for customs fraud detection using import declarations.☆30Updated 2 years ago
- Data and Model implementation for paper: FinDKG: Dynamic Knowledge Graph with Large Language Models for Global Finance☆150Updated last year