Artur Usov
ING
End-2-End Application of Machine Learning Models for Credit Acceptance Models
![](http://www.topquants.nl/wordpress/wp-content/uploads/Artur-Usov-300x300.jpeg)
The slides shown at the event can be found here.
At the event Artur presented the entire development process of machine learning models for credit acceptance models, with a focus on methods, some of the topics were:
- Risk driver engineering from big data
- Risk driver selection using traditional and ML methods Examples
- Using resemblance modelling to spot data drifts
- Temporal cross validation for PSI calculation
- ML model training & tuning
- Controlling overfit
- Using Bayesian methods for faster tuning
- Monitoring & Deployment
Artur Usov is a principal data scientist in Retail Banking Analytics Tribe, with +10 years experience of working with data & analytics in various sectors, of which 6+ years are in banking. Currently focusing on enabling Instant Lending within ING by means of Machine Leaning & Analytics. Artur has academic background is in Statistics and Economics.