Course Pack
Banking, credit, customer, fraud, and financial-services analytics demos for applied business education.
Teaching flow
This course pack collects the curated public demos for this theme and links them to the right supporting course and assignment resources.
Use the About page for context, expectations, and teaching prompts.
Move into the interactive demo only after the setup and run mode make sense for the class.
Use the linked assignment scaffold when you want written analysis, reflection, or grading.
Learning outcomes
banking analytics, fraud, lending, customer risk
Ask students to explain what action a branch, risk, or lending team should actually take after seeing the output.
Support resources
Course pages, assignments, and catalog documents should reinforce each other rather than living as separate navigation systems.
Demo catalog
Every card leads to an About page first, then to the launch surface. Run-mode and readiness badges are shown directly on the card.
About Demo learning guide for Loan Default Predictor: a browser-based banking & finance learning activity with no cloud or API keys required.
Teacher cue: Look for borrower features, predicted default risk, and threshold setting.
About Demo learning guide for Interest Rate Risk: a browser launch surface with Colab and local notebook support for deposit repricing and shock analysis.
Teacher cue: Look for deposit mix, rate shock, post-shock cost, and repricing concentration.
About Demo learning guide for Liquidity Management: a Colab and local notebook launch guide for liquidity stress testing and Monte Carlo analysis.
Teacher cue: Look for inflow and outflow stress, liquidity gap, Monte Carlo range, and action threshold.
Interactive credit scoring model for loan approval decisions. Learn machine learning in banking.
Teacher cue: Look for score drivers, risk band, and decision recommendation.
About Demo learning guide for Fraud Playground: a browser-based banking & finance learning activity with no cloud or API keys required.
Teacher cue: Look for transaction signals, anomaly score, and fraud flag.
Created by Professor Vinaya Sathyanarayana. Attribution email: vinallcontact@gmail.com. Please notify usage and retain attribution in course materials.