Credit Risk Modeling Handbook
Credit Scoring: Why it Matters and How it Works? (Part 1)
Understanding the importance and mechanics of credit scoring in managing credit risk.
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This is a tempting offer made by many financial and retail firms to their customers to increase their customer base. However, both parties need to be aware of the risks when making such credit decisions. It is important for both the lender and the customer that the customer will be able to honor the credit obligation and pay back what is owed for the purchase by the end of the loan term. Lenders need to be able to assess the risk of default for each customer, so the lender can decide to whom the offer should be granted.
What is a Credit Score?
Advances in technology have enabled financial lenders to reduce lending risk by making use of a variety of data about customers. Using statistical and machine learning techniques, available data is analyzed and boiled down to a single value known as a credit score representing the lending risk. This value can help guide the decision process. The higher the credit score the more confident a lender can be of the customer’s creditworthiness. Credit scoring is a form of Artificial Intelligence, based on predictive modeling, that assesses the likelihood of a customer defaulting on a credit obligation, becoming delinquent or insolvent. The predictive model “learns” by utilizing a customer’s historical data together with peer group data and other data to predict the probability of that customer displaying a defined behavior in the future.
The greatest benefit of credit scoring is the ability to help make decisions in a fast and efficient way, such as accepting or rejecting a customer, or increasing or decreasing loan value, interest rate, or term. The resulting speed and accuracy of making such decisions have made credit scoring the cornerstone in risk management across sectors including banking, telecom, insurance, and retail.
Credit Score Types and Customer Journey
Credit scoring can be utilized throughout the customer journey, spanning the entire customer experience during the length of the relationship between a customer and an organization. Although primarily developed for credit risk departments, marketing departments can also benefit from credit scoring techniques in their marketing campaigns.
As depicted in the figure below, different credit scores [1] are utilized at different stages of the customer journey:
- Application (origination) score assesses the risk of default of new applicants when making decisions on whether to accept or reject the applicant.
- Behavioral score assesses the risk of default associated with an existing customer when making decisions relating to account management such as credit limit, over-limit management, new products, and the like.
- Collections score is used in collections strategies for assessing the likelihood of customers in collections paying back the debt.
Credit Risk Scorecards
Over the years, a number of different modeling techniques for implementing credit scoring have evolved. They range from parametric or non-parametric, statistical, or machine learning, to supervised or non-supervised algorithms. The most recent techniques include highly sophisticated approaches utilizing hundreds or thousands of different models, various validation frameworks, and ensemble techniques with multiple learning algorithms to obtain better accuracy.
Despite such diversity, there is one modeling technique that stands out — the Credit Scorecard model. Usually referred to as the Standard Scorecard [2], it is based on logistic regression as the underlying model. Compared to other modeling techniques, this method ticks many boxes, making it the favored approach among practitioners and is used by nearly 90% of scorecard developers. A scorecard model is easy to build, understand and implement and is fast to execute. As a statistical/machine learning hybrid, its prediction accuracy is comparable to other more sophisticated techniques and its scores can be directly used as probability estimates and hence provide direct input for risk-based pricing. This is critical for lenders that comply with the Basel II regulatory framework. Being very intuitive and easy to interpret and justify, scorecards are mandated by regulators in some countries as the exclusive credit risk modeling technique.
A scorecard model result consists of a set of attributes (customer characteristics) typically displayed in tabular form. Within an attribute, weighted points (either positive or negative) are assigned to each attribute value in the range and the sum of those points equals the final credit score.
References
[1] Raymond Anderson, The Credit Scoring Toolkit, Oxford University Press (2007)
[2] Mamdouh Refaat, Credit Risk Scorecards: Development and Implementation Using SAS, Lulu.com (2011)
The Handbook Content
- Credit Scoring: Why it Matters and How it Works? (Part 1)
- Credit Scoring: Choose the Modeling Methodology Right (Part 2)
- Credit Scoring: Prepare Your Data Right (Part 3)
- Credit Scoring: Select Features that Matter (Part 4)
- Credit Scoring: Build Your Model Right (Part 5)
- Credit Scoring: Segmentation and Reject Inference — Tough Decisions (Part 6)
- Credit Scoring: Going Beyond the Modeling Basics (Part 7)
- Credit Scoring: Create Your Credit Strategy Right (Part 8)
- Credit Scoring: Model Implementation (Part 9)
- Credit Scoring: See the Bigger Picture (Part 10)
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