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Business Machine Learning and Data Science Applications

A curated list of applied business machine learning (BML) and business data science (BDS) examples and libraries. The code in this repository is in Python (primarily using jupyter notebooks) unless otherwise stated. The catalogue is inspired by awesome-machine-learning.

Finance Quant Machine Learning

Table of Contents

Department Applications


Machine Learning


  • Forensic Accounting - Collection of case studies on forensic accounting using data analysis. On the lookout for more data to practise forensic accounting, please get in touch
  • General Ledger (FirmAI) - Data processing over a general ledger as exported through an accounting system.
  • Bullet Graph (FirmAI) - Bullet graph visualisation helpful for tracking sales, commission and other performance.
  • Aged Debtors (FirmAI) - Example analysis to invetigate aged debtors.
  • Automated FS XBRL - XML Language, however, possibly port analysis into Python.

Textual Analysis

Data, Parsing and APIs

Research And Articles

  • Understanding Accounting Analytics - An article that tackles the importance of accounting analytics.
  • VLFeat - VLFeat is an open and portable library of computer vision algorithms, which has Matlab toolbox.


  • Rutgers Raw - Good digital accounting research from Rutgers.



Lifetime Value

  • Pareto/NBD Model - Calculate the CLV using a Pareto/NBD model.
  • Cohort Analysis - Cohort analysis to group customers into mutually exclusive cohorts measured over time.


  • E-commerce - E-commerce customer segmentation.
  • Groceries - Segmentation for grocery customers.
  • Online Retailer - Online retailer segmentation.
  • Bank - Bank customer segmentation.
  • Wholesale - Clustering of wholesale customers.
  • Various - Multiple types of segmentation and clustering techniques.


  • RNN - Investigating customer behaviour over time with sequential analysis using an RNN model.
  • Neural Net - Demand forecasting using artificial neural networks.
  • Temporal Analytics - Investigating customer temporal regularities.
  • POS Analytics - Analytics driven customer behaviour ranking for retail promotions using POS data.
  • Wholesale Customer - Wholesale customer exploratory data analysis.
  • RFM - Doing a RFM (recency, frequency, monetary) analysis.
  • Returns Behaviour - Predicting total returns and fraudulent returns.
  • Visits - Predicting which day of week a customer will visit.
  • Bank: Next Purchase - A project to predict bank customers' most probable next purchase.
  • Bank: Customer Prediction - Predicting Target customers who will subscribe the new policy of the bank.
  • Next Purchase - Predict a customers’ next purchase also using feature engineering.
  • Customer Purchase Repeats - Using the lifetimes python library and real jewellery retailer data analyse customer repeat purchases.
  • AB Testing - Find the best KPI and do A/B testing.
  • Customer Survey (FirmAI) - Example of parsing and analysing a customer survey.
  • Happiness - Analysing customer happiness from hotel stays using reviews.
  • Miscellaneous Customer Analytics - Various tools and techniques for customer analysis.


Churn Prediction

  • Ride Sharing - Identify customer churn rates in order to target customers for retention campaigns.
  • KKDBox I - Variational deep autoencoder to predict churn customer
  • KKDBox II - A three step customer churn prediction framework using feature engineering.
  • Personal Finance - Predict customer subscription churn for a personal finance business.
  • ANN - Churn analysis using artificial neural networks.
  • Bike - Customer bike churn analysis.
  • Cost Sensitive - Cost sensitive churn analysis drivenby economic performance.










Policy and Regulatory

Judicial Applied



  • Topic Model Reviews - Amazon reviews for product development.
  • Patents - Forecasting strategy using patents.
  • Networks - Business categories from Yelp reviews using networks can help to identify pockets of demand.
  • Company Clustering - Hierarchical clusters and topics from companies by extracting information from their descriptions on their websites
  • Marketing Management - Programmatic marketing management.

Decision Optimisation

Casual Inference


  • Various - Various applies statistical solutions


  • Applied RL - Reinforcement Learning and Decision Making tutorials explained at an intuitive level and with Jupyter Notebooks
  • Process Mining - Leveraging A-priori Knowledge in Predictive Business Process Monitoring
  • TS Forecasting - Time series forecasting for important business applications.


  • Web Scraping (FirmAI) - Web scraping solutions for Facebook, Glassdoor, Instagram, Morningstar, Similarweb, Yelp, Spyfu, Linkedin, Angellist.


Failure and Anomalies

Load and Capacity Management

Prediction Management