Overview

This is a follow up to Big Data Strategy & Roadmap - Our Data Journey blog and the focus is on different use cases that Treselle System's engineering team identified to demonstrate the basic power of Machine Learning (ML) and predictive analytics that Credit Unions can apply on their Member's data. The models are kept simple but insightful on purpose so that it is easy to understand for different CU stakeholders.

Please note that this is a mock data which was bit altered so that it is suitable for ML purpose.

Use Case 1: Member Segmentation by Product

Description:

• The objective is to understand how members are associated with different products (Checking, Savings, Auto, Home, Personal, Credit) and apply clustering to identify patterns on how members are distributed and find certain clusters for targeted marketing.
• K-means clustering algorithm was applied and identified 6 clusters based on member attributes such as products currently associated and the balance amount.
• For each cluster, the product probability is calculated based number of product count.
• Members in cluster 4 and 5 have high percentage of Savings and Checking product types but very few Loan product types. This cluster can be used for promoting loan products.
• Members in cluster 6 are strong in Savings account and can be targeted for Home Loans
• Members in cluster 1 have higher loan products but less Savings account compared to other clusters. Its important to keep an eye on this cluster for any defaults or delinquencies
• Click here for Demo URL

Use Case 2: Member Segmentation by Lifetime Value (LTV)

Description:

• Member Life Time Value is calculated on the dataset by using Acquisition cost per member, Savings account interest, Loan interest and sum of yearly transaction & other service charges.
• Categorized the members based on age group as (0-39) Generation Millennials, (39-53) Generation Xers, (53-72) Baby Boomers, (72-92) Silent Generation.
• K-means clustering algorithm was applied and identified 4 different clusters. Some clusters have members from multiple generations and few clusters have members from single generation.
• Observation:
  • 1st cluster members have high income and average CLV.
  • 2nd cluster members have high income and high CLV.
  • 3rd cluster members have less income and average CLV.
  • 4th cluster members have high income and less CLV.
  • Members in the cluster 1 & 4 are good segments to target and increase the profit
• Click here for Demo URL

Use Case 3: Advanced Targeting: Propensity Scoring of Auto Loans

Description:

• The objective of this use case is to filter all the members who have Auto Loan and train the machine learning model and apply this model on the members who don’t have Auto Loan to predict which members will opt for Auto Loan.
• Logistic regression machine learning model was used to train the data.
• Once the model was trained, the non auto loan members are used as test data and propensity scores are calculated for each members by using the trained classification model. This propensity score will give weight for each member that decides the probability of taking auto loans.
• Observation: Members who have personal loans have high propensity to opt for Auto Loans.
• Click here for Demo URL

Use Case 4: Model Comparison for Loan Eligibility

Description:

• The objective of this use case is to use DTI (Debt-to-Income ratio) and Credit Score features and compare with multiple ML models to identify the good fit model.
• 8 different machine learning models are applied on the dataset by splitting 70% & 30% to train and test the models and compared the accuracy. 
• Once the model was trained, the test data was used to perform the prediction and finally compared the accuracy based on the confusion matrix output.
• Among all models, Logistic Regression and Decision Tree was producing an accuracy of 88%.
• Click here for Demo URL