MULEORA: A Dynamically Intelligent Framework for Sequential Modeling, Deviation Characterization and Contextualized Risk Stratification of Money Mule Accounts

MULEORA, a dynamically intelligent framework for sequential modelling, deviation characterisation and contextualised risk stratification of money mule accounts.

MULEORA, a dynamically intelligent framework for sequential modelling, deviation characterisation and contextualised risk stratification of money mule accounts. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses NLTK, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.