This Point of View is focused on Credit Risk Assessment in the Consumer Lending domain including Auto, Consumer Durables and Personal loans, but excluding Mortgage. It looks at assessment using Credit Risk Modelling as well as Subjective Analysis.
Consumer lending has been beset with severe issues since the debt crisis of 2008. Rising unemployment and decreasing repayment capabilities have brought in increased risk in credit. Consumer deleveraging has also not helped increase the lending volumes of banks.
Banks are beginning to intensify the holistic assessment of risk, risk modeling as well as creation of scoring models. Historically banks had a legacy of poor credit standards, insufficient risk management and focus on volume growth. Regulation has also played an important part in an increased focus on risk and compliance.
Consumer lending in broad business terms refers to Auto finance, Personal loans, Consumer durable loans and Mortgages. This point of view focuses on Credit Risk Assessment in Consumer lending. Credit risk in Consumer Lending is defined as the possibility that a bank borrower will fail to meet his obligations in accordance with the borrowing terms. Credit Risk Assessment is a holistic approach to determine the extent of Credit Risk for each borrower.
Credit Risk Assessment in Mortgage with its distinct subject matter has not been considered in this Point of View.
For the purpose of this Point of View the scope of Credit Risk is Transaction risk or Default risk. Default risk is defined as the risk of loss when the bank considers that the obligor is unlikely to pay its credit obligations in full or is more than 90 days past due.
Delinquencies are projected to rise slightly in the US as more aggressive lending practices return to the industry. Credit Risk Assessment has increased in significance in the light of the growing defaults.
Delinquencies in US Auto Loan market are forecasted to rise slightly in the 2013–2015 timeframe as more aggressive lending practices return to the industry.
Credit Risk Assessment using Modeling
Using models and algorithms rather than human judgment is prevalent in the high volume consumer lending business. Such models and algorithms are based on factual data, e.g. data collected by credit bureau agencies. A numerical score is often the result of a model that demonstrates the creditworthiness of borrowers. The data used for analysis and scoring spans across the individual account as well as the complete spectrum of accounts held by the customer. The data indicates the customer’s past behavior with respect to the repayment.
Consumer-credit risk analytics are typically strong computational techniques for finding patterns among extremely large datasets. However purely model based or statistical methods may not provide comprehensive credit assessment and subjective (non-statistical) credit assessment is also being introduced in businesses such as Auto loans.
Credit Risk Models
There are two main approaches to credit scoring - rules based scoring and statistical methods. Rules based scoring is scoring model based on the judgment of the individuals who develop it. It provides tool as an alternative to traditional ways. In rules based scoring, the credit appraiser has to assign ratings to key factors that are critical to making the credit decision. It brings in consistency while evaluating different loan accounts.
Statistical methods focus on all variables that can indicate default using regression techniques. Statistical methods rank the variables based on their impact on the outcome. Some of the most commonly used methods include tree classification models, logistic or probit regression, neural networks, and duration models. The machine-learning algorithms include radial basis functions, tree based classifiers, and support-vector machines....