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Question: Discuss the evolution of credit risk analytics from its beginning in the 1930s; Explain what were the main factors that caused the introduction of credit risk models in the banking industry?

21 Oct 2022,12:21 AM

 

QUESTION 1 ANSWER ALL PARTS

  1. Outline the issues with traditional credit risk assessment before the introduction of models. (30)

 

  1. Explain what were the main factors that caused the introduction of credit risk models in the banking industry? (35)

 

  1. Discuss the evolution of credit risk analytics from its beginning in the 1930s. (35)

 

 

QUESTION 4

  1. State the four classification of variables for predicting LGD? (5)

 

  1. Describe how the type of debt instrument will impact the LGD. Give an example of how different type of debt instruments will influence repayment of a debt for a company in default.  (15)

 

  1. Give two examples of firm-specific variables for predicting LGD. Your answer should include the expected relationship between the variables and LGD and a rationale for this.  (40)

 

  1. Give two examples of macroeconomic-specific variables for predicting LGD. Your answer should include the expected relationship between the variables and LGD and a rationale for this.  (40)

Expert answer

 

Discuss the evolution of credit risk analytics from its beginning in the 1930s.

The origins of credit risk analytics can be traced back to the 1930s. At that time, analysts were interested in understanding how changes in economic conditions (such as the Great Depression) affected the default rates of loans. This work laid the foundation for modern credit risk models, which are used to predict the probability of default for individual borrowers.

 

Over the past few decades, credit risk analytics has evolved significantly. The introduction of computers and data science techniques has allowed for more sophisticated models to be developed. Today, credit risk analytics is used by lenders to assess the creditworthiness of borrowers and make decisions about whether to extend credit. It is also used by investors to identify opportunities and manage portfolios.

 

Looking to the future, it is likely that credit risk analytics will continue to evolve and become more sophisticated. The application of machine learning techniques, for example, could enable lenders to better assess the risk of default and make more informed decisions about lending. As data becomes more readily available, it is also likely that credit risk analytics will be used in new and innovative ways to provide insights into credit risk.

 

Explain what were the main factors that caused the introduction of credit risk models in the banking industry?

Credit risk models were introduced in the banking industry in response to a number of factors, including the need for banks to better manage their credit portfolios, the increased complexity of financial products, and the global nature of banking. Credit risk models help banks to quantify and manage the risk of losses from defaults on loans and other obligations. They are an important tool for managing credit portfolios and setting lending policies. There are a variety of credit risk models in use today, each with its own strengths and weaknesses. Banks must carefully select the model that best suits their needs and objectives.

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