fraud-detection

Open-source projects categorized as fraud-detection
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Topics: Machine Learning fraud-prevention anomaly-detection Data Mining outlier-detection
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Top 23 fraud-detection Open-Source Projects

  • fingerprintjs

    Browser fingerprinting library. Accuracy of this version is 40-60%, accuracy of the commercial Fingerprint Identification is 99.5%. V4 of this library is BSL licensed.

    • Project mention: Should I Open Source my Company? | news.ycombinator.com | 2024-01-22

  • pyod

    A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

    • Project mention: A Comprehensive Guide for Building Rag-Based LLM Applications | news.ycombinator.com | 2023-09-13

    This is a feature in many commercial products already, as well as open source libraries like PyOD. https://github.com/yzhao062/pyod

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • anomaly-detection-resources

    Anomaly detection related books, papers, videos, and toolboxes

    • Project mention: anomaly-detection-resources: NEW Extended Research - star count:7507.0 | /r/algoprojects | 2023-10-24

  • MISP

    MISP (core software) - Open Source Threat Intelligence and Sharing Platform

    • Project mention: A recent abrupt change in Internet SSH brute force attacks against us | news.ycombinator.com | 2024-02-24

  • awesome-fraud-detection-papers

    A curated list of data mining papers about fraud detection.

    • Project mention: awesome-fraud-detection-papers: NEW Extended Research - star count:1346.0 | /r/algoprojects | 2023-05-13

  • graph-fraud-detection-papers

    A curated list of graph-based fraud, anomaly, and outlier detection papers & resources

  • pygod

    A Python Library for Graph Outlier Detection (Anomaly Detection)

    • Project mention: RAG Using Structured Data: Overview and Important Questions | news.ycombinator.com | 2024-01-10

    Ok, using ChatGPT and Bard (the irony lol) I learned a bit more about GNNs:

    GNNs are probabilistic and can be trained to learn representations in graph-structured data and handling complex relationships, while classical graph algorithms are specialized for specific graph analysis tasks and operate based on predefined rules/steps.

    * Why is PyG it called "Geometric" and not "Topologic" ?

    Properties like connectivity, neighborhoods, and even geodesic distances can all be considered topological features of a graph. These features remain unchanged under continuous deformations like stretching or bending, which is the defining characteristic of topological equivalence. In this sense, "PyTorch Topologic" might be a more accurate reflection of the library's focus on analyzing the intrinsic structure and connections within graphs.

    However, the term "geometric" still has some merit in the context of PyG. While most GNN operations rely on topological principles, some do incorporate notions of Euclidean geometry, such as:

    - Node embeddings: Many GNNs learn low-dimensional vectors for each node, which can be interpreted as points in a vector space, allowing geometric operations like distances and angles to be applied.

    - Spectral GNNs: These models leverage the eigenvalues and eigenvectors of the graph Laplacian, which encodes information about the geometric structure and distances between nodes.

    - Manifold learning: Certain types of graphs can be seen as low-dimensional representations of high-dimensional manifolds. Applying GNNs in this context involves learning geometric properties on the manifold itself.

    Therefore, although topology plays a primary role in understanding and analyzing graphs, geometry can still be relevant in certain contexts and GNN operations.

    * Real world applications:

    - HuggingFace has a few models [0] around things like computational chemistry [1] or weather forecasting.

    - PyGod [2] can be used for Outlier Detection (Anomaly Detection).

    - Apparently ULTRA [3] can "infer" (in the knowledge graph sense), that Michael Jackson released some disco music :-p (see the paper).

    - RGCN [4] can be used for knowledge graph link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes).

    - GreatX [5] tackles removing inherent noise, "Distribution Shift" and "Adversarial Attacks" (ex: noise purposely introduced to hide a node presence) from networks. Apparently this is a thing and the field is called "Graph Reliability" or "Reliable Deep Graph Learning". The author even has a bunch of "awesome" style lists of links! [6]

    - Finally this repo has a nice explanation of how/why to run machine learning algorithms "outside of the DB":

    "Pytorch Geometric (PyG) has a whole arsenal of neural network layers and techniques to approach machine learning on graphs (aka graph representation learning, graph machine learning, deep graph learning) and has been used in this repo [7] to learn link patterns, also known as link or edge predictions."

    --

    0: https://huggingface.co/models?pipeline_tag=graph-ml&sort=tre...

    1: https://github.com/Microsoft/Graphormer

    2: https://github.com/pygod-team/pygod

    3: https://github.com/DeepGraphLearning/ULTRA

    4: https://huggingface.co/riship-nv/RGCN

    5: https://github.com/EdisonLeeeee/GreatX

    6: https://edisonleeeee.github.io/projects.html

    7: https://github.com/Orbifold/pyg-link-prediction

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • ThreatIngestor

    Extract and aggregate threat intelligence.

  • DGFraud

    A Deep Graph-based Toolbox for Fraud Detection

  • fraud-detection-handbook

    Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

  • getIPIntel

    IP Intelligence is a free Proxy VPN TOR and Bad IP detection tool to prevent Fraud, stolen content, and malicious users. Block proxies, VPN connections, web host IPs, TOR IPs, and compromised systems with a simple API. GeoIP lookup available.

    • Project mention: getIPIntel: NEW Extended Research - star count:243.0 | /r/algoprojects | 2023-04-29

  • TabFormer

    Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)

    • Project mention: Time-based splitting performing significantly worse than random splitting | /r/learnmachinelearning | 2023-05-20

    Hi, I am currently working on a basic binary classifier for a transaction dataset, to predict which transaction is fraudulent (Dataset: https://github.com/IBM/TabFormer). The following is a quick summary of the dataset:

  • fraud-detection-using-machine-learning

    Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker

  • Free-RASP-Community

    SDK providing app protection and threat monitoring for mobile devices, available for Flutter, Cordova, Android and iOS.

    • Project mention: Attempt#2 - HELP! I'm looking for beta testers for my app, and would be great if this post doesn't get deleted | /r/algeria | 2023-06-10

    if i may ask, how did you test the app? would you recommend this?

  • MLSys-NYU-2022

    Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022

    • Project mention: Where to start | /r/mlops | 2023-09-13

    There are 3 courses that I usually recommend to folks looking to get into MLE/MLOps that already have a technical background. The first is a higher-level look at the MLOps processes, common challenges and solutions, and other important project considerations. It's one of Andrew Ng's courses from Deep Learning AI but you can audit it for free if you don't need the certificate: - Machine Learning in Production For a more hands-on, in-depth tutorial, I'd recommend this course from NYU (free on GitHub), including slides, scripts, full-code homework: - Machine Learning Systems And the title basically says it all, but this is also a really good one: - Hands-on Train and Deploy ML Pau Labarta, who made that last course, actually has a series of good (free) hands-on courses on GitHub. If you're interested in getting started with LLMs (since every company in the world seems to be clamoring for them right now), this course just came out from Pau and Paul Iusztin: - Hands-on LLMs For LLMs I also like this DLAI course (that includes Prompt Engineering too): - Generative AI with LLMs It can also be helpful to start learning how to use MLOps tools and platforms. I'll suggest Comet because I work there and am most familiar with it (and also because it's a great tool). Cloud and DevOps skills are also helpful. Make sure you're comfortable with git. Make sure you're learning how to actually deploy your projects. Good luck! :)

  • cryptowallet_risk_scoring

    A free cryptowallet risk scoring tool with fully explainable scoring.

  • realtime-fraud-detection-with-gnn-on-dgl

    An end-to-end blueprint architecture for real-time fraud detection(leveraging graph database Amazon Neptune) using Amazon SageMaker and Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a Graph Neural Network(GNN) model to detect fraudulent transactions in the IEEE-CIS dataset.

    • Project mention: realtime-fraud-detection-with-gnn-on-dgl: NEW Extended Research - star count:165.0 | /r/algoprojects | 2023-05-20

  • benford_py

    Python implementation of Benford's Law tests.

  • UGFraud

    An Unsupervised Graph-based Toolbox for Fraud Detection

  • Marble

    Marble - the real time decision engine for fraud and AML (by checkmarble)

    • Project mention: Marble – Open-Source real-time fraud and AML monitoring | news.ycombinator.com | 2024-01-31

  • FraudDetection

    Accounting Fraud Detection Using Machine Learning

  • threatbite

    ThreatBite is a real-time service that detects unwanted web users.

  • MemStream

    MemStream: Memory-Based Streaming Anomaly Detection

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

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NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).

fraud-detection related posts

Index

What are some of the best open-source fraud-detection projects? This list will help you:

Project Stars
1 fingerprintjs 20,938
2 pyod 7,941
3 anomaly-detection-resources 7,871
4 MISP 4,986
5 awesome-fraud-detection-papers 1,545
6 graph-fraud-detection-papers 1,263
7 pygod 1,208
8 ThreatIngestor 786
9 DGFraud 655
10 fraud-detection-handbook 429
11 getIPIntel 306
12 TabFormer 295
13 fraud-detection-using-machine-learning 248
14 Free-RASP-Community 241
15 MLSys-NYU-2022 238
16 cryptowallet_risk_scoring 220
17 realtime-fraud-detection-with-gnn-on-dgl 202
18 benford_py 148
19 UGFraud 123
20 Marble 124
21 FraudDetection 111
22 threatbite 86
23 MemStream 81

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