What is Survival Analysis?
While the term might invoke visions of Bear Grylls type survival skills for the wilderness, survival analysis is actually a statistical technique that can be used in financial risk management to estimate the likelihood of an event occurring over time. It is commonly used to analyse the time it takes for a customer to default on a loan or make a payment after becoming delinquent.
One of the primary benefits of using survival analysis in credit risk management is that it allows lenders to identify high-risk customers and take proactive measures to mitigate losses. By analysing the probability of default over time, lenders can develop a better understanding of which customers are most likely to default and when this is likely to occur. This can be useful in developing targeted collections strategies and early warning systems to alert lenders when a customer is at risk of defaulting.
Survival analysis can also be used to analyse the impact of various risk factors on the likelihood of default. For example, lenders can use survival analysis to assess the impact of income level, credit score, and other variables on the probability of default over time. This can help lenders identify the factors that are most strongly correlated with default risk and develop more effective underwriting criteria.
One of the primary limitations of survival analysis is that it assumes that the risk factors are constant over time. In reality, the risk factors that affect credit risk can flux as customers experience changes in income, employment status, and other factors. This means that survival analysis may not always provide an accurate estimate of default risk.
Survival analysis also requires a large amount of data to be effective. This means that lenders with smaller loan portfolios may not have enough data to generate accurate survival curves. In these cases, lenders may need to rely on other statistical techniques to estimate default risk.
A useful tool in credit risk management, survival analysis can help identify high-risk customers, develop targeted collections strategies and assess the impact of various risk factors on default risk. However, it is important to keep in mind that survival analysis has limitations and may not always provide an accurate estimate of default risk. Lenders should also be aware of the data requirements and ensure that they have enough data to generate accurate survival curves before using this technique.