A repository dedicated to sharing the AML/ALL related public information, papers, code and datasets that we come across through R&D.

A comparative study of ML algorithms for anti-money laundering (AML) detection using the IBM AML dataset. Implemented Decision Trees, Random Forests, XGBoost, LGBM, SGD and SVM to evaluate model performance on imbalanced data with feature engineering techniques.

FinAUDIT is an AI-powered financial data health and compliance system that automatically audits datasets against global regulatory standards (GDPR, Visa CEDP, AML, PCI DSS, and Basel). It combines a deterministic 30-rule engine for rigorous data quality scoring with a Generative AI Analyst (Gemini) to provide natural-language answer.

AML Dataset

This project is based on Gene expression dataset from Kaggle. Here Molecular Classification of Cancer by Gene Expression monitoring Dataset is done. This dataset comes from a proof-of-concept study published in 1999 by Golub et al. It showed how new cases of cancer could be classified by gene expression monitoring (via DNA microarray) and thereby provided a general approach for identifying new cancer classes and assigning tumors to known classes. These data were used to classify patients with acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL).

🧬 91.56% accuracy cancer classification (AML vs ALL) using gene expression | 6 ML algorithms, PCA, SHAP analysis | Golub dataset (7,129 genes)

OmniGuard AML is an AI-driven data policy engine that uses Google's Gemini to extract executable rules from natural-language regulatory documents and scans massive transaction datasets via DuckDB to detect money laundering at scale.

Blood cancer is an uprising issue and doing physical medical procedures is too sensitive and time-consuming to detect any blast cell. Manual testing includes blood tests, spinal fluid tests, bone marrow tests, imaging tests, etc. A solution to this is to use modern methods in health care that help to detect diseases faster and increase the cure rate.ssing and Transfer Learning for Detection of Types of Leukemia: In image processing, data preparation and image preprocessing are done where we have rescaled the image and adjusted the brightness to improve the image quality. Data augmentation is performed to increase the image count by flipping it horizontally and vertically. Images are converted to grayscale to reduce the matrix calculation.The images in the dataset are: AML has 935 images, ALL has 858, CML has 623 and CLL has 510. Transfer learning is used. I have used different pre-trained CNN models such as ResNet-50, VGG16, Inception V3, and MobileNet for feature extraction and classification.VGG16, InceptionV3 and MobileNet - all three models give 100% accuracy, while ResNet50 gives 85% accuracy.

Real cutting data collected on Kinova Jaco 2 robot

End-to-end KYC/AML compliance data analysis using mock datasets. Includes customer risk scoring, suspicious transaction flagging, and compliance reporting in Python (Pandas, Matplotlib).

datasets for aml hw

No description available

No description available

Dataset Analysis and ML Models for predicting cancer AML and ALL cancer types

Implementation of AML lecture 9/10 KMeans Clustering (Q's 2-12) for the Bot-IoT dataset. Rewritten to NOT require pyspark.

End-to-end AML transaction anomaly detection pipeline using Isolation Forest, LOF, Random Forest, and XGBoost on the PaySim synthetic financial dataset

Project includes categorization of acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) using Principal Component Analysis and sorting algorithms on datasets of gene sequencing.(In Progress)

Linear Regression Model analyzing the IBM Transactions for Anti Money Laundering (AML) dataset from Kaggle. Model accurately predicts (RMSE = 0.032) whether a given bank transaction is an example of money laundering activity.

Financial Intelligence Unit (FIU) case study on the PaySim synthetic transactions dataset. Featuring SQL and Python (Pandas, NumPy, Scikit-learn, Matplotlib) workflows for anomaly detection, AML threshold analysis, and financial crime data visualization.

Support Vector Regression (SVR) model predicting drug sensitivity in Acute Myeloid Leukemia (AML) patients using only genetic profiles from the GDSC2 dataset. Achieved R² of 0.95 (validation) and 0.89 (test), showing strong potential for precision oncology and personalized treatment.

Challenge organized by the École Normale Supérieure (ENS) and Qube Research & Technologies (QRT). The objective of this project is to predict the overall survival of patients with Acute Myeloid Leukemia (AML). The dataset is complex, multimodal (clinical data, genomic mutations, cytogenetic reports), and subject to right-censorship.

One of the worst epidemics in the history of mankind is the deadly disease known as cancer. There are several types of cancer and the one that is more commonly heard of these days is leukemia. There are two types of leukemia – acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL) – and the purpose of this study is to take into account the gene expression data of several people and predict what type of leukemia they have by using three machine learning algorithms, XGBoost, Random Forest Classification and Artificial Neural Networks. The dataset’s dimensionality was reduced using principal component analysis (PCA) before using the algorithms on them.

AML Dataset

AML dataset

AML dataset generator

AML Scenario Datasets

No description available

No description available

No description available

Dataset on Anti-Money Laundering