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.