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Webinar Series : Credit Risk Trends and Analytics

Ensure profitability in times of uncertainty.

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With the onset of uncertain times, lenders need to be extra cautious as delinquencies and default rates tend to increase in a recession. Safeguarding your portfolio from such economic shocks should be your top priority. That means you require a throughout data-driven approach for credit-risk assessment. Then your credit-decision model should be the reflection of the same and needs to be continually fined tuned. Altair with Deep Credit Risk brings Credit Risk Trends and Analytics, a three-part webinar series on credit risk analytics with a focus on the pain points of developing models that reflect the driving economics.

 

Data preparation for income and expense shocks

With the right data, in this webinar, you will learn to build credit risk models through machine learning techniques and understand the impact of the economic downturn on the model outcome. You will develop a good understanding of the pitfalls of machine learning and the merits relative to econometric regression techniques. You will be able to predict defaults, payoffs, loss rates and exposures. You will be able to efficiently validate credit risk models. We will compare and interpret various validation strategies. You will learn how to build meaningful user interfaces, compare outputs for models in terms of stability, discrimination, and calibration and efficiently communicate outputs.
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Build modern credit-risk models using Machine Learning

With the right data, in this webinar, you will learn to build credit risk models through machine learning techniques and understand the impact of the economic downturn on the model outcome. You will develop a good understanding of the pitfalls of machine learning and the merits relative to econometric regression techniques. You will be able to predict defaults, payoffs, loss rates and exposures. You will be able to efficiently validate credit risk models. We will compare and interpret various validation strategies. You will learn how to build meaningful user interfaces, compare outputs for models in terms of stability, discrimination, and calibration and efficiently communicate outputs.
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Visualise and validate credit risk models

With this webinar, you will be able to efficiently validate credit risk models. We will compare and interpret various validation strategies. You will learn how to compare outputs for models: stability, discrimination, and calibration. You will learn to visualize and efficiently communicate model performance.
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Speakers

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Harald Scheule

Professor of Finance at the University of Technology, Sydney.

Professor Harald is a strategic partner for banks and regulators in Asia, Australia, Europe, and North America. He is a specialist in Banking, Credit and Liquidity Risk, Housing Finance, and Machine Learning. He has had influence with financial institutions that have applied his work to improve their risk management practices. He currently serves on the editorial board of the Journal of Risk Model Validation. Harry is a dedicated educator, who consistently receives excellent student feedback, and his PhD students have produced impactful industry research. Harry's textbooks on credit risk analytics are used around the world in data analytics courses.

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Dr. Clinton Chee

Solutions Specialist/Data Scientist, Altair

PhD in Shape Control of Smart Structures in the Aeronautical Engineering Department at the University of Sydney. Degrees in Mechanical Engineering and Science (Maths/Physics) at the University of Melbourne. Clinton has been working in scientific computing/programming including modifying programs to run on supercomputers, developing web-based software for e-Research, and working as a quantitative analyst at Australia's No.1 bank. There he re-coded the Operational Risk models which took a week to run down to a few hours.

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