Build a Credit-Card Fraud Detector for a Singapore Neobank
Overview
What this challenge is about.
Train logistic regression and gradient-boosted models on 8M transactions, calibrate probabilities, and choose operating points to earn a verifiable certificate.
The scenario
The neobank serves around 70,000 SMEs across Singapore, Malaysia, and the Philippines; card fraud loss is around SGD 2.4 million per year and false-positive declines cost an estimated SGD 6 million in lost interchange and churn.
The Brief
What you'll do, and what you'll demonstrate.
Deliver a calibrated fraud-detection model and a deployment plan that beats the current rules on either fraud capture or false-positive rate.
Earning criteria — what you'll demonstrate
- Train classification models under severe class imbalance honestly
- Calibrate model probabilities so thresholds mean what they say
- Translate a model into one or two business-meaningful operating points
- Plan deployment artifacts (monitoring, retraining cadence) before shipping
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Classification Modeling
Apply classification modeling to solve real industry problems and demonstrate production-level capability.
- Class Imbalance
Apply class imbalance to solve real industry problems and demonstrate production-level capability.
- Model Calibration
Apply model calibration to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Owning a binary classifier from raw data to a business-defensible threshold is the bread-and-butter project a junior data scientist ships in a fintech risk team.
This challenge sharpens
- classification-modeling
- feature-engineering
- model-evaluation
Machine Learning Engineer
The deployment plan plus calibrated model is the handoff package an MLE turns into a real-time scoring service.
This challenge sharpens
- model-calibration
- python
- model-evaluation