EXPERIAN WEBINARS

Machine Learning for Credit Risk

Take a deep dive into the world of machine learning, specifically tailored for financial services where governance is paramount. Discover a comprehensive range of best practices for building, fine-tuning and deploying robust and effective ML models. Explore critical strategies for monitoring and retraining these models to adapt to evolving risk landscapes.

Dive into how your organization can:
  • Optimize use of machine learning models for managing credit risk
  • Better monitor and retrain models by identifying early signs of deterioration
  • Incorporate cross-validation and hyper-parameter tuning specifically for credit risk scenarios
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SPEAKERS

Sagar Bathla
Director, Data Modeling
Experian
Sagar is the Director of Data Modeling at Experian North America, where he leads a team of 15 data scientists. The team is primarily focused on various custom modeling, business intelligence and data intelligence projects for acquisition, customer management and portfolio management models. His team also focuses on research work on advancements in risk modeling using machine learning techniques.

Sagar has over 14 years of experience in the financial industry in credit risk management and healthcare analytics. Before joining Experian, he was a consultant at EXL, leading client engagements for large U.S. banks in credit risk modeling and strategy. He also worked at Aon Hewitt in healthcare analytics and Bank of America in mortgage analytics and credit card campaign management.

Mark Soffietti
Senior Director, Decision Analytics
Experian
Mark has over 15 years of experience in the industry transforming data into actionable knowledge for effective decision management with the majority of those years spent rising through the ranks at Experian.

Through a wide range of large engagements, he has participated in all departments of financial institutions. Mark’s experience includes developing solutions for consumer and commercial lending across the credit spectrum – from marketing to collections. His background spans multiple industry sectors including banking, automotive, and retail.

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