Detect Coordinated Fraud Rings via Link Analysis at a Neobank
Overview
What this challenge is about.
Analyze 30M banking events to build a link graph, detect fraud rings with community detection, and generate a top-100 suspicion list. Get a verifiable certificate.
The scenario
The neobank (around 700 staff, around 800,000 active users) lost around EUR 7 million to synthetic-identity fraud last year and is under pressure from BaFin (the German financial regulator) to demonstrate stronger detection.
The Brief
What you'll do, and what you'll demonstrate.
Surface coordinated synthetic-identity fraud rings via graph and link analysis, ranked for investigator triage at precision@50 above 0.6.
Earning criteria — what you'll demonstrate
- Construct multi-relation graphs from operational data
- Apply community detection at fintech scale and reason about resolution
- Engineer ring-level features that align with investigator intuition
- Communicate model output to a non-ML investigative audience
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.
- Graph Analysis
Apply graph analysis to solve real industry problems and demonstrate production-level capability.
- Community Detection
Apply community detection to solve real industry problems and demonstrate production-level capability.
- Link Analysis
Apply link analysis to solve real industry problems and demonstrate production-level capability.
- Fraud Detection
Apply fraud detection to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Feature Engineering
Apply feature engineering 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
Fraud-ring detection with graphs is a high-leverage data-scientist specialization at any fintech or marketplace; this challenge demonstrates the full workflow.
This challenge sharpens
- graph-analysis
- link-analysis
- fraud-detection
Applied AI Scientist
Translating community detection into investigator-actionable rationale is the kind of last-mile work applied AI scientists own.
This challenge sharpens
- community-detection
- fraud-detection
- feature-engineering
Machine Learning Engineer
Productionizing graph features and scoring pipelines is increasingly a junior MLE responsibility on financial-crime teams.
This challenge sharpens
- python
- feature-engineering
- graph-analysis