How to reimagine risk modelling in sustainable finance
From 8-9 December 2020, Finextra and ResponsibleRisk brought together experts in the sustainable finance space to discuss how stakeholders can re-imagine the role of risk modelling.
Climate risk vs. sustainability risk: Is there a difference?
Are you prepared for the risks and liability of extreme weather and #ESG obligations? Businesses need to assess various factors of climate and sustainability risks to navigate them effectively. Watch the video and learn more about our framework: https://bit.ly/3QeWDnr
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Understanding How To Implement Sustainable Business Models | BCG
BCG Senior Partners Wendy Woods and David Young discuss how businesses must rethink the way they deliver capitalism to win the ‘20s. Business models must become more aligned with the broader set of customer, employee, and environmental needs. By looking through the lens of the Sustainable Development Goals, companies can better analyze their business, capabilities, customers, and suppliers.
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R tutorial: Intro to Credit Risk Modeling
Learn more about credit risk modeling with R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r
Hi, and welcome to the first video of the credit risk modeling course. My name is Lore, I’m a data scientist at DataCamp and I will help you master some basics of the credit risk modeling field.
The area of credit risk modeling is all about the event of loan default. Now what is loan default? When a bank grants a loan to a borrower, which could be an individual or a company, the bank will usually transfer the entire amount of the loan to the borrower. The borrower will then reimburse this amount in smaller chunks, including some interest payments, over time. Usually these payments happen monthly, quarterly or yearly. Of course, there is a certain risk that a borrower will not be able to fully reimburse this loan. This results in a loss for the bank.
The expected loss a bank will incur is composed of three elements. The first element is the probability of default, which is the probability that the borrower will fail to make a full repayment of the loan. The second element is the exposure at default, or EAD, which is the expected value of the loan at the time of default. You can also look at this as the amount of the loan that still needs to be repaid at the time of default. The third element is loss given default, which is the amount of the loss if there is a default, expressed as a percentage of the EAD. Multiplying these three elements leads to the formula of expected loss. In this course, we will focus on the probability of default.
Banks keep information on the default behavior of past customers, which can be used to predict default for new customers. Broadly, this information can be classified in two types. The first type of information is application information. Examples of application information are income, marital status, et cetera. The second type of information, behavioral information, tracks the past behavior of customers, for example the current account balance and payment arrear history. Let’s have a look at the first ten lines of our data set.
This data set contains information on past loans. Each line represents one customer and his or her information, along with a loan status indicator, which equals 1 if the customer defaulted, and 0 if the customer did not default. Loan status will be used as a response variable and the explanatory variables are the amount of the loan, the interest rate, grade, employment length, home ownership status, the annual income and the age. The grade is the bureau score of the customer, where A indicates the highest class of creditworthiness and G the lowest. This bureau score reflects the credit history of the individual and is the only behavioral variable in the data set.
For an overview of the data structure for categorical variables, you can use the CrossTable() function in the gmodels package. Applying this function to the home ownership variable, you get a table with each of the categories in this variable, with the number of cases and proportions. Using loan status as a second argument, you can look at the relationship between this factor variable and the response. By setting prop.r equal to TRUE and the other proportions listed here equal to FALSE, you get the row-wise proportions. Now what does this result tell you? It seems that the default rate in the home ownership group OTHER is quite a bit higher than the default rate in, for example, the home ownership group MORTGAGE, with 17.5 versus 9.8 percent of defaults in these groups, respectively. Now, let’s explore other aspects of the data using R.
Climate Risk Modelling: Understanding the Knowns and Unknowns
In an age of persistent climate risk, innovative approaches to data tools and predictive modelling are critical. In this Aon Insights Series Pacific 2021 session, our experts discuss the need for an integrated approach to assessing climate-related physical, transition and litigation risks.