Build an MLP Baseline for Credit-Default Risk at a Fintech
Overview
What this challenge is about.
Train an MLP with regularization on credit-default data, compare to XGBoost with SHAP, and earn a verifiable certificate.
The scenario
The fintech (around 320 staff, around 1.4 million customers in the UK and Ireland) wants one deep-learning model footprint instead of three XGBoosts but needs CRO sign-off on parity or better risk metrics.
The Brief
What you'll do, and what you'll demonstrate.
Match or beat XGBoost on AUC, calibration, and approval-rate-at-default-rate using a single regularized MLP.
Earning criteria — what you'll demonstrate
- Apply regularization (dropout, weight decay) on tabular MLPs
- Compare deep models against strong tree baselines fairly
- Evaluate calibration on a credit-risk model
- Communicate model behavior to a CRO audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Deep Learning
Master · Deep Learning
Strong alignment
This challenge maps to Deep Learning at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Mlp
Apply mlp to solve real industry problems and demonstrate production-level capability.
- Regularization
Apply regularization to solve real industry problems and demonstrate production-level capability.
- Tabular Deep Learning
Apply tabular deep learning to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Calibration
Apply calibration to solve real industry problems and demonstrate production-level capability.
- Shap
Apply shap 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:
Machine Learning Engineer
Replacing tree baselines with calibrated MLPs and writing the CRO memo is exactly the kind of first project a junior MLE owns at a fintech.
This challenge sharpens
- mlp
- regularization
- calibration
Data Scientist
Model comparison with calibration and SHAP-based explanations is a canonical credit-risk data-scientist deliverable.
This challenge sharpens
- calibration
- shap
- tabular-deep-learning
Applied AI Scientist
Translating deep-learning parity into a CRO sign-off package mirrors the applied-AI-scientist's bridging role.
This challenge sharpens
- mlp
- calibration
- shap