Credit Risk Modelling Training Course Online: DexLab Analytics

Online Courses

Credit Risk Modeling with Machine Learning

DexLab Analytics Credit Risk Modeling and Scorecards

70 hours

Live Instructor led classroom & Self-Paced

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What you will learn in this course:

  • Business Scorecards: Acquisition and Behaviour Scorecards
  • BASEL AIRB Models: Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD) models.
  • Provision Models: Allowance for Loans and Lease Loss Models using Expected Loss Approach
  • Tools: Python
  • Machine Learning Algorithm: Decision Tree, Random Forest, XGBoost, Support Vector Machine
  • Basic descriptive analysis
  • Basic statistical interference
  • Basic predictive modeling techniques
  • Python Programming
  • Regulatory Risk Model development and Validation
  • Understanding of scorecard model vocabulary
  • Detailed training on scorecard model development
  • Credit risk regulatory guidelines with BASEL II
  • Regulatory stress testing guidelines with DFAST and CCAR

Credit Risk Modeling at DexLab Analytics has undergone a complete industrial upgradation. The new certification module, called Credit Risk Modeling with Machine Learning, is now inclusive of latest industry trends and implementation. Year 2020 is being touted as the year of Machine Learning implementation into risk analytics. As an organization we strive towards betterment and making you industry ready.

The module is a perfect blend of theoretical and practical implementation of key credit risk scorecards and regulatory models on the Python platform. The primary objective of the module is to provide an exhaustive discussion on the development and validation of key models used extensively by risk managers.

This course will offer you an opportunity to understand the measure of central tendency, measures of dispersion, probability theory and probability distribution, sampling techniques, estimation theory, types of statistical tests, linear regression, logistic regression, application of machine learning algorithm such as Decision tree, Random Forest, XGBoost, Support Vector Machine, banking products and processes, need for scorecards, uses of scorecard, scorecard model development, use of scorecard for designing business strategies of a bank, LGD, PD, EAD and much more.

This course will also teach students Risk Analytics, Application Scorecard and Behavioral Scorecard (BAU Model), Regulatory Requirement and PG, LGD, EAD Model Development.

With a certification in Credit Risk Management a student will gain proficiency in understanding and usage of the basic credit risk management tools. Secure competitive advantage for your company with enhanced credit risk management techniques.

The trainer is a bachelors in Computer Science with more than half a decade of experience in to Credit Risk, Analytics and Predictive modelling, worked previously with companies like Moody’s, GE Capital, Standard Chartered. Currently working with a leading global bank, he has an extensive knowledge of Scorecards, Regulatory model development, deployment & validation with emphasis on using state of the art machine learning techniques using Python.

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Credit Risk Modeling with Machine Learning

Credit Risk Modeling with Machine Learning

Online Training

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