MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 413 MB | Duration: 1h 54m
Every time an institution extends a loan, it faces credit risk. It is the risk of economic loss when an obligor does not fulfill the terms and conditions of his contracts. Measuring and managing credit risk is imperative to financial organizations as this information exposes the creditworthiness of the borrowers and helps banks lower the risk of default.
Over the last decade, a number of the world's largest banks have developed sophisticated systems in an attempt to model the credit risk arising from important aspects of their business lines. Financial institutions make use of vast amounts of data on borrowers and loans and apply these predictive and analytical models. Such models are intended to aid banks in quantifying, aggregating, and managing risk across geographical and product lines.
The outputs of these models also play increasingly important roles in banks' risk management and performance measurement processes, including performance-based compensation, customer profitability analysis, risk-based pricing, active portfolio management, and capital structure decisions.
In this class, our objective is to learn how to build these credit risk models using R Programming. While credit risk arises in almost all business lines for a bank, our focus will be on the credit risk involved in the personal and corporate loans, which is of major importance to banks.
What's Included
Detailed concepts and explanations about each topic
Step-by-step instructions for all models built in R
Output Interpretation and insights
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