← Case Studies
Financial Services⏱ Under 4 hours· Mid-Sized Financial Services Firm
Automated ML Pipeline
End-to-end fraud detection ML pipeline for a financial services firm — multi-model training, hyperparameter optimization, and automated ensembling, delivered before a regulatory deadline.
Results
< 4 hrs
Delivery Time
From kickoff to a fully trained, submission-ready fraud detection model
79.4%
Model Accuracy
Final ensemble model accuracy across the held-out validation set
The Challenge
A mid-sized financial services firm needed a fraud detection ML pipeline to meet regulatory deadlines, but their data science team was backlogged for months.
What Arkitekt Built
- ◆Automated data discovery, profiling, and feature engineering
- ◆Multi-model training across 5 algorithms with stratified cross-validation
- ◆Hyperparameter optimization across 50+ parameter combinations
- ◆Automated model ensembling and submission-ready predictions